IEEE Sensors Letters最新文献_第2页

Tampon-Based Sensors for Monitoring Intravaginal pH Levels

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-07 DOI: 10.1109/LSENS.2025.3549185

Goran M. Stojanović;Saima Qureshi;Hima Zafar;Dušica Dimitrijević;Ivana Tomić

{"title":"Tampon-Based Sensors for Monitoring Intravaginal pH Levels","authors":"Goran M. Stojanović;Saima Qureshi;Hima Zafar;Dušica Dimitrijević;Ivana Tomić","doi":"10.1109/LSENS.2025.3549185","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3549185","url":null,"abstract":"The proposed work is a proof-of-concept study aimed at developing a sensor capable of measuring and monitoring vaginal pH. The objective of this research is to examine the design concepts and operational principles of a vaginal sensor that utilizes soft, biodegradable textile materials. Traditional methods of assessing vaginal pH levels, such as evaluating vaginal fluids or discharge from the vaginal walls, are difficult to perform consistently over long periods and are often prone to errors. Our sensor demonstrates a simple fabrication technique of pH value monitoring based on the capacitive principle by embroidering the sensors onto a tampon, which is comfortable and biocompatible. Silver conductive threads, known for their antibacterial properties, are used. Two different geometries of embroidered electrodes are proposed for the pH sensors, enabling the monitoring of vaginal pH. A device that tracks changes in pH value aids in early detection, notifies users of potential health changes, supports women's self-management of wellness, and helps prevent conditions, such as vaginitis or sexually transmitted infections associated with pH levels above 4.7.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Machine Learning and Sensor-Driven System for Nondestructive Detection of Jaggery Adulteration 用于无损检测掺假琼脂的新型机器学习和传感器驱动系统

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-07 DOI: 10.1109/LSENS.2025.3548887

Vinayak Bairagi;Vaishali H. Kamble;Sharad T Jadhav;Mrinal R Bachute

{"title":"A Novel Machine Learning and Sensor-Driven System for Nondestructive Detection of Jaggery Adulteration","authors":"Vinayak Bairagi;Vaishali H. Kamble;Sharad T Jadhav;Mrinal R Bachute","doi":"10.1109/LSENS.2025.3548887","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3548887","url":null,"abstract":"Food adulteration is a major challenge on a global scale impacting 10% of the food supply and leading to financial losses up to $30–40 billion annually. A developing country, such as India, is also not an exception to this widespread concerning issue and has significant adulteration cases reported across various categories, including Jaggery, which is its major product sharing 55% of the total world Jaggery production. While the literature reports a few methods for detecting various food adulterations, jaggery—the most popular food in India—has received meagre attention. Moreover, the reported methods have limited success and need further experimentation on a variety of diverse datasets before they are practically deployable. This research presents a classical, novel color-based method for detecting the adulteration in the jaggery. A color sensor is used to detect the color of melted jaggery samples, and an Arduino Uno is used to further analyze the color for reliable detection of adulteration. This research exploits the direct relationship between the captured pixel intensities of the jaggery and its purity to develop a linear regression model. The developed product is validated using samples having varying percentages of adulterations (10%–70%) caused due to single and multiple adulterants (sugar and food color) in jaggery. The machine learning-based novel approach developed in this research gives promising results with an accuracy of 94.67% and a precision as 92.6%. The developed method for identifying tampered jaggery is user-friendly, affordable, portable, and nondestructive and the experimental results confirm its superiority.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698318","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

Wearable Multisensory Glove for Shape, Size, and Stiffness Recognition Based on Off-the-Shelf Components

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-05 DOI: 10.1109/LSENS.2025.3548264

Mohamad Yaacoub;Ali Ibrahim;Fatima Khansa;Leila Hammadi;Christian Gianoglio

引用次数: 0

FPGA-Based Real-Time Road Object Detection System Using mmWave Radar

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3547008

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

{"title":"FPGA-Based Real-Time Road Object Detection System Using mmWave Radar","authors":"Anand Mohan;Hemant Kumar Meena;Mohd Wajid;Abhishek Srivastava","doi":"10.1109/LSENS.2025.3547008","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3547008","url":null,"abstract":"This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment's point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667612","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

Thin-Film Magneto-Optic Plasmonic Sensor With the Photonic Spin Hall Effect

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3547745

Harshit Shukla;Alka Verma;Anuj K. Sharma;Yogendra Kumar Prajapati

引用次数: 0

IDMatchHAR: Semi-Supervised Learning for Sensor-Based Human Activity Recognition Using Pretraining

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3546985

Koki Takenaka;Shunsuke Sakai;Tatsuhito Hasegawa

{"title":"IDMatchHAR: Semi-Supervised Learning for Sensor-Based Human Activity Recognition Using Pretraining","authors":"Koki Takenaka;Shunsuke Sakai;Tatsuhito Hasegawa","doi":"10.1109/LSENS.2025.3546985","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3546985","url":null,"abstract":"In sensor-based human activity recognition (HAR), the annotation cost for sensor data is higher compared to data, such as images. One can use semisupervised learning (semi-SL) to reduce annotation costs. This method lever-ages unlabeled datasets by assigning pseudolabels. How- ever, these methods have the issue of confirmation bias, where performance degrades due to incorrect pseudolabels. Some approaches have attempted to solve this problem by performing multistage pretraining with labeled and unlabeled data, but these methods require significant computational resources. We propose a framework called IDMatchHAR, which performs semi-SL with a single-stage pretraining process on small-scale datasets. We use instance discrimination (ID) during pretraining to learn robust feature representations applied to the subsequent semi-SL task. We verify the effectiveness of the proposed framework using various convolutional neural networks (CNNs), such as VGG and residual network (ResNet), as well as Transformers, on HASC, WISDM, and Pamap2. Our proposed framework significantly reduces the computational cost of pretraining while demonstrating performance comparable to or exceeding that of existing semi-SL methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645167","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

A Charge Balanced Stimulators With a Twin Compensation Loop for Evoked Neural Potential Sensing

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-28 DOI: 10.1109/LSENS.2025.3546239

Longbin Zhu;Wenjie Wang;Risheng Su;Zhijun Zhou;Keping Wang

{"title":"A Charge Balanced Stimulators With a Twin Compensation Loop for Evoked Neural Potential Sensing","authors":"Longbin Zhu;Wenjie Wang;Risheng Su;Zhijun Zhou;Keping Wang","doi":"10.1109/LSENS.2025.3546239","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3546239","url":null,"abstract":"Neurological functional electrical stimulation (NFES) provides a well-controlled and high degree of reconfigurability amount for producing the desired stimulation effect. For evaluating the effect of stimulation, the evoked potentials can be sensed. However, in practice, the unbalanced charge accumulation leads to residual charges and toxic electrochemical reactions at the electrode-tissue sensing interface, which causes electrode corrosion and tissue damage. This letter presents a twin compensation loop (TCL) charge-balancing (CB) topology for the NFES. The TCL-CB consists of two compensation loops, including a positive compensation loop (PCL) and a negative compensation loop (NCL). The PCL and NCL continuously detect and compensate for the accumulated negative and positive charges at the output of the Wilson current mirror (WSC), respectively. The circuits are targeted at integrated circuit (IC) realization, designed and fabricated in a 0.18-<italic>μ</i>m complementary metal oxide semiconductor (CMOS) technology. The power consumption of the WSC with TCL-CB is circa 79.2 μW and occupies a die area of 0.047 mm<sup>2</sup> (0.265 μm × 0.18 μm). Benefiting from TCL-CB, the charge mismatch at the output of WSC is reduced to less than 1%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645302","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

Optimal Load Type for Passive Magnetic Energy Harvesters 无源磁能采集器的最佳负载类型

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-27 DOI: 10.1109/LSENS.2025.3546246

Alon Kuperman

引用次数: 0

Localization of RFID Tags Through Real-Time Angle-of-Arrival Estimation

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-26 DOI: 10.1109/LSENS.2025.3546092

Antonello Florio;Gianfranco Avitabile;Giuseppe Coviello

引用次数: 0

A Lightweight Causal Sound Separation Model for Real-Time Hearing Aid Applications