IEEE Sensors Letters最新文献_第7页

Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition 用于人体呼吸监测和声纹识别的快速响应碳纳米管基光纤传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-20 DOI: 10.1109/LSENS.2024.3446853

Sunil Mohan;Manish Singh Negi

{"title":"Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition","authors":"Sunil Mohan;Manish Singh Negi","doi":"10.1109/LSENS.2024.3446853","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3446853","url":null,"abstract":"This letter describes the development of a simple and novel optical fiber relative humidity (RH) sensor to be used for breath monitoring and voice recognition. The proposed sensor utilizes an intensity modulation phenomenon via evanescent wave (EW) absorption. The optical fiber sensor (OFS) employs a chemically synthesized nanostructured sensing film composed of multiwalled-carbon-nanotube-doped chitosan coated over a 5-cm length of a centrally decladded, straight, and uniform plastic cladding silica (PCS) fiber. A comprehensive experimental investigation is carried out to analyze the response characteristics of the proposed sensor. A linear response over the dynamic range of ∼70–97% RH with a sensitivity of 0.3041 dB/% RH is observed for the developed sensor. Furthermore, the resolution of the developed RH sensor is observed to be ±0.13% RH. An average response and recovery times of 100 and 150 ms are recorded during the humidification and dehumidification process. In addition, the proposed sensor demonstrates a high degree of repeatability, reversibility, and stability. Moreover, the developed sensor has the ability to detect RH fluctuations within exhaled air during both breathing and speaking.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090984","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

Visualization Study for Enhancing the Efficiency of Local Damage Diagnosis on Flat Belts Based on MFL Technology 基于 MFL 技术提高平带局部损伤诊断效率的可视化研究

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-20 DOI: 10.1109/LSENS.2024.3446698

Sung-Won Kim;Sae-Byeok Kyung;Eun-Yul Lee;Ju-Won Kim

{"title":"Visualization Study for Enhancing the Efficiency of Local Damage Diagnosis on Flat Belts Based on MFL Technology","authors":"Sung-Won Kim;Sae-Byeok Kyung;Eun-Yul Lee;Ju-Won Kim","doi":"10.1109/LSENS.2024.3446698","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3446698","url":null,"abstract":"Flat belts are increasingly used in elevators, which offer faster stabilization and energy savings compared to wire ropes. How- ever, damage to flat belts during operation can lead to catastrophic accidents, such as rope failure and falls due to tensile loads. Therefore, there is a need for monitoring techniques to detect damage in advance and prevent accidents. Although extensive research has been conducted on the diagnosis of damage to wire ropes, studies on diagnosing damage to flat belts are lacking. In this letter, we propose a monitoring technique that applies the magnetic flux leakage (MFL) method to diagnose flat belt damage. MFL sensors utilizing permanent magnets were tailored for flat belts to measure the leakage flux. Six instances of artificial damage were created using samples of flat belts actually used in elevators, with damage induced at 30-cm intervals. Subsequently, MFL sensors were used to measure the leakage flux, confirming its occurrence in the damaged areas. Furthermore, as the degree of damage increased, the size of the leakage flux also increased. These findings confirm the potential of using MFL sensors for damage diagnosis through monitoring.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174022","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

Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks 利用心电图传感和优化深度网络自动评估孕期微压力

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-19 DOI: 10.1109/LSENS.2024.3444810

Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy

{"title":"Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks","authors":"Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy","doi":"10.1109/LSENS.2024.3444810","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3444810","url":null,"abstract":"Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077613","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

Single-Mode-Multimode-Single-Mode Fiber (SMS): Exploring Environmental Sensing Capabilities 单模-多模-单模光纤（SMS）：探索环境传感功能

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-16 DOI: 10.1109/LSENS.2024.3445153

Silvia Diaz;Miguel Ángel Armendáriz;Ignacio R. Matías

引用次数: 0

Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities 用于工业设施声学状态监测的敲击声探测技术

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-16 DOI: 10.1109/LSENS.2024.3445162

C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter

引用次数: 0

Photovoltaic-Energy-Powered Temperature-Sensing Chip With Digital Output and Built-in Energy Harvesting Circuit 具有数字输出和内置能量收集电路的光伏发电温度传感芯片

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-14 DOI: 10.1109/LSENS.2024.3443274

Yen-Ju Lin;Jian-Zhou Yan;Kai-Min Chang;Chia-Ling Wei

{"title":"Photovoltaic-Energy-Powered Temperature-Sensing Chip With Digital Output and Built-in Energy Harvesting Circuit","authors":"Yen-Ju Lin;Jian-Zhou Yan;Kai-Min Chang;Chia-Ling Wei","doi":"10.1109/LSENS.2024.3443274","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443274","url":null,"abstract":"In this letter, a temperature-sensing chip with a built-in photovoltaic (PV) energy harvesting circuit is proposed. The temperature-sensing circuit includes a bipolar-junction-transistor (BJT)-based sensing circuit, a gain stage, and a successive approximation register (SAR) analog-to-digital converter (ADC), while the energy harvesting circuit is a boost dc–dc converter with a perturbation-and-observation maximum power point tracking circuit. The main goal of this work is successful chip integration. To the best of our knowledge, this is the first chip that integrates a temperature sensor, an ADC, an energy harvesting circuit, a clock generator, and other related circuits into a single chip. While conventional temperature-sensing chips are typically powered by a stable power supply voltage (which may not be available in Internet of Things devices), the proposed chip is powered by the built-in boost converter, whose output voltage inevitably has ripples. Despite this, the performance of our temperature-sensing chip is excellent. In addition, the built-in clock generator can generate signals with a subhertz frequency, which is difficult to achieve with low-power requirements. The chip was fabricated using the TSMC 0.18-μm 1P6M mixed-signal process. The measured results indicate that the sensed temperature of the proposed chip ranges from –20 °C to 80 °C with 0.17 °C resolution. The error is within ±0.8 °C, and \u0000<italic>R</i>\u0000<sup>2</sup>\u0000 representing linearity reaches 0.99988.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084486","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

Revealing the Capability of an LMR Microfluidic Biosensor for Celiac Disease Diagnosis via Label-Free Detection of Antigliadin Antibodies 通过无标签检测抗花粉蛋白抗体揭示 LMR 微流控生物传感器诊断乳糜泻的能力

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-14 DOI: 10.1109/LSENS.2024.3443461

Melanys Benitez;Pablo Zubiate;Abián B. Socorro;Ignacio R. Matías

引用次数: 0

Generating Synthetic Mechanocardiograms for Machine Learning-Based Peak Detection 生成合成机械心电图，用于基于机器学习的峰值检测

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-14 DOI: 10.1109/LSENS.2024.3443526

Jonas Sandelin;Ismail Elnaggar;Olli Lahdenoja;Matti Kaisti;Tero Koivisto

{"title":"Generating Synthetic Mechanocardiograms for Machine Learning-Based Peak Detection","authors":"Jonas Sandelin;Ismail Elnaggar;Olli Lahdenoja;Matti Kaisti;Tero Koivisto","doi":"10.1109/LSENS.2024.3443526","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443526","url":null,"abstract":"Acquiring labeled data for machine learning algorithms in healthcare is expensive due to the laborious expert annotation and privacy concerns. This challenge is further complicated in the case of mechanocardiogram (MCG) data, which are characterized by high interpersonal and intrapersonal complexity, compounded further by sensor variability. In this letter, we introduce an innovative method for generating synthetic MCG signals to address the scarcity of labeled data necessary for training machine learning models in healthcare. Our approach involves generating RR-intervals, adding wavelets, and incorporating noise to create realistic synthetic MCG signals. These synthetic signals were used to train a convolutional neural network for peak detection in real MCG data. Our key contributions include developing a detailed methodology for realistic synthetic MCG signal generation, reducing the mean absolute error in peak detection by 4.88 beats per minute using synthetic data, enhancing the training of machine learning models, creating a new peak detection method, and addressing data scarcity in biomedical signal processing. These contributions emphasize the methodological innovations and the significance of our results, underscoring the potential impact of synthetic data in improving healthcare diagnostics.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328418","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

GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor GajGamini：利用基于地面振动的地震传感器探测移动中的大象，缓解人与动物的冲突

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-13 DOI: 10.1109/LSENS.2024.3442830

Chandan;Mainak Chakraborty;Sahil Anchal;Bodhibrata Mukhopadhyay;Subrat Kar

{"title":"GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor","authors":"Chandan;Mainak Chakraborty;Sahil Anchal;Bodhibrata Mukhopadhyay;Subrat Kar","doi":"10.1109/LSENS.2024.3442830","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3442830","url":null,"abstract":"We introduce “GajGamini:” a novel method for detecting elephant movement by analyzing ground vibrations recorded using seismic sensors. This method is based on the principle that ground vibrations from elephants are distinct from those caused by humans and background noise. In this letter, we address two main challenges. First, there was a lack of studies with extensive data on vibrations from Indian elephants and humans. To address this, we recorded 3 h of elephant movements and 2 h of human movements using seismic sensors. Second, there was a need for a dedicated architecture for the real-time classification of seismic vibrations from elephants, humans, and background noise. To overcome this, we propose a convolutional neural network (CNN)–based model named “GajGamini” that achieves a prediction accuracy of \u0000<inline-formula><tex-math>${sim}98.03%$</tex-math></inline-formula>\u0000 with only 3 s of computational runtime for every 10 s of recorded data. GajGamini represents a significant advancement in wildlife monitoring, particularly for elephant conservation. It offers a noninvasive way to track elephant movements, enhancing the effectiveness of wildlife management strategies.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090983","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

Attention-Based 2-D Hand Keypoints Localization 基于注意力的二维手部关键点定位

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-13 DOI: 10.1109/LSENS.2024.3443072

H Pallab Jyoti Dutta;M. K. Bhuyan

引用次数: 0