IEEE Sensors Letters最新文献_第9页

Machine Learning Assisted Pb2+ Ions Detection Using AlGaN/GaN HEMT Sensors 机器学习辅助Pb2+离子检测的AlGaN/GaN HEMT传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-12 DOI: 10.1109/LSENS.2026.3652747

Nidhi Mahesh Hegde;Pramod Martha;Adarsh Nigam

{"title":"Machine Learning Assisted Pb2+ Ions Detection Using AlGaN/GaN HEMT Sensors","authors":"Nidhi Mahesh Hegde;Pramod Martha;Adarsh Nigam","doi":"10.1109/LSENS.2026.3652747","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3652747","url":null,"abstract":"In this work, a novel machine learning (ML)-integrated AlGaN/GaN high electron mobility transistor (HEMT)-based sensor is reported for the detection of Pb2+ ion contamination in aqueous media. Here, the gate region of the HEMT was functionalized with 2,5-dimercapto-1,3,4-thiadiazole (DMTD) to provide high affinity and selectivity toward Pb22+ ions. The DMTD functionalization enabled the device to exhibit excellent sensitivity at pH 5.5. Moreover, the lifetime stability analysis indicated consistent sensing performance over a period of 30 days. Furthermore, ML-based methodology was applied to detect the presence of Pb2+ ions contamination in solution by analyzing the sensing response of AlGaN/GaN HEMT sensor. Here, a Random Forest classifier was trained on the obtained sensing response. The dataset was preprocessed, balanced using synthetic minority oversampling technique to counteract class imbalance, and split into training (80% ) and testing (20% ) sets. The classifier achieved an accuracy of 90.50%, precision of 1.0 recall of 81.03%, and F1-score of 89.52% . Visualization through confusion matrix, receiver operating characteristic curves, and learning curves also demonstrates the robustness and reliability of the proposed model. The integration of ML with DMTD functionalized AlGaN/GaN HEMT sensing platforms establishes a promising pathway for rapid, selective, and stable Pb2+ ion detection in water quality monitoring applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223703","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

Error Analysis in Detection of Soil Moisture Using Triad Spectroscopy Sensor 三联光谱传感器检测土壤水分的误差分析

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-12 DOI: 10.1109/LSENS.2026.3652134

Koustuvmoni Bharadwaj;Krishna Bharadwaj;Kalyan Kumar Das

{"title":"Error Analysis in Detection of Soil Moisture Using Triad Spectroscopy Sensor","authors":"Koustuvmoni Bharadwaj;Krishna Bharadwaj;Kalyan Kumar Das","doi":"10.1109/LSENS.2026.3652134","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3652134","url":null,"abstract":"Soil moisture value is critical to the global hydrological cycle and impacts agriculture, environmental science, urban planning, forestry, and more. Traditional methods of measuring soil moisture, though well established, are time consuming, labor intensive, requires installation and maintenance of equipment, expensive and gives low resolution outputs. This calls for the requirement of a soil moisture calculation method which is instantaneous, portable, economic and efficient. Spectroscopy has emerged as a promising solution since it can deliver valuable insights into the composition, structure, and properties of materials across diverse fields. In this study, a spectroscopic sensor was employed to analyze soil samples, and its readings were compared with soil moisture values determined through the conventional gravimetric method. By applying different regression approaches, a relationship between the sensor output and soil moisture was established, aiming to minimize errors, such as mean absolute error (MAE), mean bias error (MBE), and root mean square error (RMSE). The findings revealed that a third-order polynomial regression equation at 705 nm wavelength provided the most accurate results, with error values of MAE, MBE, and RMSE being 0.38, 0.06, and 0.5, respectively. This outcome demonstrates that combining the spectroscopic sensor with the derived equation can offer an instantaneous, noncontact method for soil moisture estimation. Compared to the lengthy and labor-intensive gravimetric process, this approach provides a rapid and practical solution for soil moisture assessment in various applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082247","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

High-Resolution LiDAR via Upsampling Multiecho Data Obtained From Low-Resolution SPAD Arrays 通过上采样从低分辨率SPAD阵列获得多回波数据的高分辨率激光雷达

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-12 DOI: 10.1109/LSENS.2026.3652223

Tzu-Hsien Sang;Tzu-Ching Lin

引用次数: 0

Neck EMG-Based Estimation of Upper-Limb Muscle Tension 基于颈部肌电图的上肢肌肉张力评估

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-12 DOI: 10.1109/LSENS.2026.3652230

Arinobu Niijima

引用次数: 0

Deep Learning-Based Multiradar Fusion for Robust Real-Time Object Detection 基于深度学习的多雷达融合鲁棒实时目标检测

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-05 DOI: 10.1109/LSENS.2025.3650621

Philipp Reitz;Tobias Veihelmann;Jonas Bönsch;Norman Franchi;Maximilian Lübke

引用次数: 0

An MAML-Based Lightweight Neural Network With Domain Adaptation for Cross-Subject and Generalized EEG-Based Motor Imagery Classification 基于mml的轻量级神经网络领域自适应跨主题广义脑电运动图像分类

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-05 DOI: 10.1109/LSENS.2026.3650795

Aaqib Raza;Mohd Zuki Yusoff

引用次数: 0

Tactile Recognition Using PVDF Sensor Arrays With Time-Series Image Encoding and CNN 基于时间序列图像编码和CNN的PVDF传感器阵列触觉识别

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-05 DOI: 10.1109/LSENS.2025.3650702

Tao Cao;Hongfei Cao;Ang Chen;Xinglin Zhang;Shuchen Bai

{"title":"Tactile Recognition Using PVDF Sensor Arrays With Time-Series Image Encoding and CNN","authors":"Tao Cao;Hongfei Cao;Ang Chen;Xinglin Zhang;Shuchen Bai","doi":"10.1109/LSENS.2025.3650702","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3650702","url":null,"abstract":"To enhance the recognition performance of flexible tactile sensing systems in human–computer interaction, this letter proposes a tactile signal recognition method for polyvinylidene fluoride (PVDF) sensor arrays based on time-series imaging. In this study, a 4 × 4 PVDF sensor array was constructed for signal acquisition. The key innovation is the conversion of preprocessed voltage time series into two distinct image representations—Gramian Angular Field (GAF) and Markov Transition Field (MTF) images—to fully exploit the dynamic features of the signals. These images are then fed into a convolutional neural network (CNN) for end-to-end classification. Experimental results demonstrate that the proposed method achieves outstanding performance in contact state recognition, with both the GAF+CNN and MTF+CNN models exceeding 95% in accuracy, precision, recall, and F1-score. The MTF+CNN model shows a slight advantage in recall and F1-score. In terms of deployment, the system achieves millisecond-level single-sample inference latency on a general-purpose computing platform (MacBook Pro 2022, M2 chip), proving its potential for real-time practical applications. This work provides an effective solution for developing high-precision low-latency tactile sensing systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082169","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

Calibration and Performance Evaluation of Low-Cost Air Quality Sensors in an Urban Environment of Western India 印度西部城市环境中低成本空气质量传感器的校准和性能评估

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-05 DOI: 10.1109/LSENS.2026.3650975

Yash Dahima;Tapaswini Sarangi;Yogeshkumar Patel;Lokesh Kumar Sahu;Narendra Ojha;Aditya Vaishya

{"title":"Calibration and Performance Evaluation of Low-Cost Air Quality Sensors in an Urban Environment of Western India","authors":"Yash Dahima;Tapaswini Sarangi;Yogeshkumar Patel;Lokesh Kumar Sahu;Narendra Ojha;Aditya Vaishya","doi":"10.1109/LSENS.2026.3650975","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3650975","url":null,"abstract":"Confidence in the use of low-cost sensors (LCS), as a viable alternative to expensive research-grade instruments, is increasing for air quality monitoring due to low-budget and ease of deployment. However, numerous studies have suggested significant variations in their performances under varying environmental conditions, therefore highlighting the need of detailed evaluations and careful calibrations prior to their applications for the region of interest. Such studies have been relatively few in India and particularly lacking in the semiarid urban environments of western India. In this regard, we calibrated LCS for measurements of particle size distribution (OPC-N3) and ozone (O3) (Alphasense OXB4) utilizing reference-grade measurements (GRIMM, Thermo), and have evaluated the performance of LCS over Ahmedabad. For computing PM2.5, the corrections have been derived from the particle mass size distribution, which improved the accuracy significantly compared to the reference measurements (R2 ∼ 0.7, normalized mean absolute bias ∼ 29% ). O3 variability is calibrated using reference O3 and sensor-measured temperature and relative humidity, with the aid of machine learning. Measurements from the two O3 sensors showed good intercorrelation and agreement with the reference (R2 ∼ 0.7). Our study fills a gap of calibration and performance evaluation of LCSs in a distinct urban environment of western India and highlights the need for careful corrections in order to have reliable air quality measurements. LCS-based measurements were found to capture typical features of the urban air quality in this region, and therefore can be deployed to quantify trends and to understand the important factors governing aerosols and O3. The study can serve as a reference for future developments toward the low-cost comprehensive measurements of atmospheric composition, including other key air pollutants.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026445","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

Resonance-Based Sensing on Large Surfaces Using RF Excitation for Human–Machine Interaction 基于射频激励的人机交互大表面共振传感

IF 2.2

IEEE Sensors Letters Pub Date : 2026-01-01 DOI: 10.1109/LSENS.2025.3650141

Vikas Kumar;Shivansh Awasthi;Vikash Sharma;Santosh Parajuli;Thomas George Thundat;Ankur Gupta

{"title":"Resonance-Based Sensing on Large Surfaces Using RF Excitation for Human–Machine Interaction","authors":"Vikas Kumar;Shivansh Awasthi;Vikash Sharma;Santosh Parajuli;Thomas George Thundat;Ankur Gupta","doi":"10.1109/LSENS.2025.3650141","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3650141","url":null,"abstract":"This letter proposes resonance-based sensing on large surfaces using 13.56 MHz RF excitation to enhance human safety in human–machine interaction (HMI). In this approach, whole large conducting surface itself functions as a sensor by varying its resonance characteristics in response to object interactions. The sensing system is compact in design and optimized for operation in the industrial, scientific, and medical band. The surface is excited using a single conductor powered resonant coil, which provides good passive voltage gain at −16 dBm input power. A secondary resonant coil is employed to amplify voltage variations caused by human touch on the surface. The system delivers a high voltage output signal that can be directly interfaced with a computing platform through an analog to digital converter, thereby eliminating the need for external amplifiers or analog filters. This technique offers a cost effective, low power, and large area sensing solution for human robot collaboration. The proposed system can be seamlessly integrated into machines or robotic platforms for sensing applications in HMI.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982310","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

FPGA Implementation of Binary-Weighted Transformer for Prognostics and Health Management of Permanent Magnet Synchronous Motors Using Current Sensors 基于电流传感器的永磁同步电机预测与健康管理二值加权变压器的FPGA实现

IF 2.2

IEEE Sensors Letters Pub Date : 2025-12-31 DOI: 10.1109/LSENS.2025.3649802

Soongyu Kang;Yongchul Jung;Sewoon Oh;Yunho Jung

{"title":"FPGA Implementation of Binary-Weighted Transformer for Prognostics and Health Management of Permanent Magnet Synchronous Motors Using Current Sensors","authors":"Soongyu Kang;Yongchul Jung;Sewoon Oh;Yunho Jung","doi":"10.1109/LSENS.2025.3649802","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3649802","url":null,"abstract":"In this letter, we propose a prognostics and health management (PHM) method for permanent magnet synchronous motors (PMSMs) in urban air mobility. The method uses a Transformer and three-phase current sensors. The short-time Fourier transform is employed for current signals to capture fault-related information. The Transformer architecture effectively extracts both local and global features from time–frequency representations, enabling high classification performance. However, its high computational cost and large model size hinder deployment in practical industrial applications. To address this challenge, we designed a lightweight binary-weighted Transformer (BWT) for PMSM PHM, reducing the model size to 5.5% of the baseline. The proposed BWT achieves 99.81% classification accuracy across four classes. We also developed a hardware accelerator for matrix multiplication—the most time-consuming operation in BWT—and implemented it on a field-programmable gate array. The proposed SW/HW co-design achieved an 85.55× speedup over the software-only implementation on the ARM microprocessor unit.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982360","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