IEEE Sensors Letters最新文献_第3页

Extended Dual-Message QR Codes With Visual Secret Sharing 扩展双消息QR码与视觉秘密共享

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-14 DOI: 10.1109/LSENS.2025.3560787

Ran-Zan Wang;Liang-Yi Wang;Kun Chen

{"title":"Extended Dual-Message QR Codes With Visual Secret Sharing","authors":"Ran-Zan Wang;Liang-Yi Wang;Kun Chen","doi":"10.1109/LSENS.2025.3560787","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3560787","url":null,"abstract":"The camera serves as one of the primary sensors enabling smartphones to interact with the physical world, making it an essential tool for various interactive applications. By exploring the camera's unique way to recognize module bit values in the QR code decoding algorithm, this letter enhances dual-message QR codes by integrating visual secret sharing (VSS) functionalities. Each QR code encodes two distinct messages that can be read by standard QR code readers: one readable at close range and the other at a distance. In addition, the proposed (<italic>t, <italic>n) VSS scheme allows the confidential message to be revealed by stacking <italic>t or more of the <italic>n QR codes, while fewer than <italic>t QR codes disclose no information about the secret. Readability tests confirm that both messages in each QR code are accessible via standard smartphone QR code apps, and that the confidential message is recognizable to the human eye. Security analyses validate that the confidential message is reliably protected. Compared to previously reported multilayer QR codes, the proposed scheme encodes three layers of messages within a single QR code, archiving a higher information payload. Moreover, the capability to visually reveal the additional confidential message by stacking QR codes without requiring any computational processing is a notable advantage.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888447","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

Classification of Fiber Optics Anomalies Using Transforms Ensemble, Adaptive Smoothing Based on the Standardized Variable Distances Learning Algorithm and Convolutional Neural Networks 基于变换集成、标准化变距离学习自适应平滑和卷积神经网络的光纤异常分类

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-11 DOI: 10.1109/LSENS.2025.3560144

Gianmarco Baldini

引用次数: 0

An Efficient Multiphase Integration-Type Linear Digital Thermistor Front-End 一种高效的多相集成型线性数字热敏电阻前端

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-10 DOI: 10.1109/LSENS.2025.3559993

Safeer S S;Anoop Chandrika Sreekantan;Radhika V. N.

引用次数: 0

Printed Single-Chip RFID Tags on Uncoated Paper for Environmental Monitoring Applications 用于环境监测应用的无涂布纸单芯片RFID标签

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-10 DOI: 10.1109/LSENS.2025.3559556

Lukas Rauter;Lukas Neumaier;Tutku Bedük;Martin Lenzhofer;Arnold Horn;Muhammad Hassan Malik;Johanna Zikulnig;Razvan Oltean;Albert Seiler;Jürgen Kosel

{"title":"Printed Single-Chip RFID Tags on Uncoated Paper for Environmental Monitoring Applications","authors":"Lukas Rauter;Lukas Neumaier;Tutku Bedük;Martin Lenzhofer;Arnold Horn;Muhammad Hassan Malik;Johanna Zikulnig;Razvan Oltean;Albert Seiler;Jürgen Kosel","doi":"10.1109/LSENS.2025.3559556","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3559556","url":null,"abstract":"The growing demand for sustainable and efficient environmental monitoring systems has driven the development of innovative sensor technologies. This study presents a hybrid ultra-high frequency radio-frequency identification (RFID) sensor tag fabricated on uncoated paper substrate, which constitutes approximately 87% of the tag's mass thereby making the sensor tag more sustainable and eco-friendly. The sensor tag integrates an AS3213C.4 RFID chip together with an antenna, an interdigitated capacitor as a humidity sensor, pads, and interconnects. Temperature sensing is facilitated by the RFID chip's internal temperature sensor, while humidity is monitored through changes in the printed capacitor. All structures except for the chip were screen printed using a conductive silver ink. The silver layer exhibited a thickness of 5.6 μm and a sheet resistance of 56.4 mΩ/sq, sufficient for wireless communication over a distance of 2 m. The sensor was wirelessly interrogated using a Kathrein antenna and reader system, with data retrieved via commercial software. Temperature tests demonstrated accurate readings from 26 °C to 80 °C, aligning with the chip's specifications of −40 °C to 125 °C, with a precision of 1 °C in the range of 10 °C to 50 °C. Humidity measurements in a climate chamber, conducted between 15% and 55% relative humidity, showed an average sensitivity of 0.45% per % humidity change. Hysteresis effects of 7.4% were observed due to the moisture absorption and structural changes of the paper substrate. This work highlights the potential of paper-based sensor tags for sustainable environmental monitoring, aligning with the principles of Industry 4.0 and the Internet of Things (IoT), while addressing the growing challenge of electronic waste.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892435","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

Enhancing Extraversion Classification With Sample Entropy: A Comparison of Two EEG Epoch Lengths 利用样本熵增强外向性分类：两种脑电信号Epoch长度的比较

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-10 DOI: 10.1109/LSENS.2025.3559549

Nur Syahirah Roslan;Ibrahima Faye;Hafeez Ullah Amin;Muhamad Hafiz Abd Latif

{"title":"Enhancing Extraversion Classification With Sample Entropy: A Comparison of Two EEG Epoch Lengths","authors":"Nur Syahirah Roslan;Ibrahima Faye;Hafeez Ullah Amin;Muhamad Hafiz Abd Latif","doi":"10.1109/LSENS.2025.3559549","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3559549","url":null,"abstract":"With the advancement of technology, many researchers have begun to employ electroencephalography (EEG) to assess extraversion personality traits, replacing subjective self-report questionnaires. However, most EEG studies are time-consuming and have inadequate classification accuracy. Thus, this letter proposes a framework for extraversion classification using sample entropy features extracted from resting-state EEG signals. The proposed framework compares two different EEG epoch lengths (15 and 120 s) and evaluates their impact on classification performance. To enhance the classification performance, a sequential forward selection method is applied to ensure that only the most optimal features are utilized. Using support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting as classifiers, the study shows that sample entropy outperforms power and coherence features in classifying extraversion. Remarkably, the framework achieves 100% classification accuracy using a single feature: the sample entropy from a 15-s eyes-open condition at the Fpz electrode. By reducing the number of required features to just one and focusing on a shorter EEG epoch length, this finding reflects the potential of developing EEG-based sensor systems that are more practical and cost-effective in the future.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883450","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

DeepApneaNet: A Multistage CNN-Bi-LSTM Hybrid Model for Sleep Apnea Detection From Single-Lead ECG Signal DeepApneaNet：用于单导联心电信号睡眠呼吸暂停检测的多级CNN-Bi-LSTM混合模型

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-08 DOI: 10.1109/LSENS.2025.3558675

Imran Hossan;Muhammad Sudipto Siam Dip;Sumaiya Kabir;Mohammod Abdul Motin

{"title":"DeepApneaNet: A Multistage CNN-Bi-LSTM Hybrid Model for Sleep Apnea Detection From Single-Lead ECG Signal","authors":"Imran Hossan;Muhammad Sudipto Siam Dip;Sumaiya Kabir;Mohammod Abdul Motin","doi":"10.1109/LSENS.2025.3558675","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3558675","url":null,"abstract":"Obstructive sleep apnea (OSA) is a critical sleep disorder that can lead to severe health complications and even death if left untreated. Early OSA detection through non-invasive methods, such as single-lead electrocardiogram (ECG) analysis, presents a promising approach for timely intervention. In contrast to the existing single-stage convolutional neural network (CNN) and bidirectional long short-term memory-based (BiLSTM) hybrid models, this letter presents DeepApneaNet, a novel end-to-end deep learning framework that combines multiple CNN-BiLSTM-based hybrid subnetworks in a cascaded manner to detect OSA from single-lead ECG signals. With the PhysioNet Apnea-ECG Database, our implemented framework is able to achieve the best per-segment accuracy, sensitivity, and specificity of 88.61%, 84.23%, and 91.04%, respectively. For per recording classification, our model achieved 94.29% accuracy, 100% sensitivity, and 83.33% specificity. Even though using minimal preprocessing and without any hand-crafted feature extraction, the performance of our model is still comparable to the state-of-the-art methodologies. The results indicate that segmenting hybrid models into smaller networks enhances the understanding of sequence dynamics. DeepApneaNet demonstrates significant potential as a practical solution for diagnosing OSA in real-world settings.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900545","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

Multisensor-Based Fault Detection Method for Air-Exchange Device in EMU Train 基于多传感器的动车组换气装置故障检测方法

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-08 DOI: 10.1109/LSENS.2025.3558957

Rongjiao Wei;Pu Wei;Chao Pan;Yangyang An;Hao Zhu;Mulan Wang

{"title":"Multisensor-Based Fault Detection Method for Air-Exchange Device in EMU Train","authors":"Rongjiao Wei;Pu Wei;Chao Pan;Yangyang An;Hao Zhu;Mulan Wang","doi":"10.1109/LSENS.2025.3558957","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3558957","url":null,"abstract":"In this letter, we introduce and experimentally demonstrate a fault detection method for the air-exchange devices in the electric multiple unit (EMU) train, which utilizes the abnormal sound and vibration generated by the devices when the faults occur. The sound and vibration signals are fused, and the time–frequency matrix is extracted using a short-time Fourier transform (STFT). Fault recognition is performed using the trained support vector machine (SVM) classifier. A sound detection system is built for experiments, in which the package of the sound sensor is designed to shield the sound from adjacent devices. The system includes an field programmable gate array (FPGA) and an embedded system and can be used for fault detection in the future. The experiment shows that the accuracy of the fused signals is higher than the single sensor, up to 0.995. In addition, the performances of the algorithm are evaluated, and the precision, recall, accuracy, and F1-score are all up to 0.99, which meet the actual fault detection requirements. Our method effectively improves the efficiency and accuracy of fault detection for the air-exchange device and can be widely used in the EMU train.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883346","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

Integration of Bionic Olfactory Model With MEMS Sensor Array Enhances Odor Classification 仿生嗅觉模型与MEMS传感器阵列的集成增强了气味分类

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-08 DOI: 10.1109/LSENS.2025.3558967

Chen Luo;Yujie Yang;Dongcheng Xie;Zhe Wang;Yongfei Zhang;Xiaolei Shen;Lei Xu

{"title":"Integration of Bionic Olfactory Model With MEMS Sensor Array Enhances Odor Classification","authors":"Chen Luo;Yujie Yang;Dongcheng Xie;Zhe Wang;Yongfei Zhang;Xiaolei Shen;Lei Xu","doi":"10.1109/LSENS.2025.3558967","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3558967","url":null,"abstract":"This letter presents a solution that integrates a microelectromechanical systems sensor array with a bionic olfactory model (BOM) to simplify data processing and enhance odor classification accuracy. The integrated sensor array adopts a quadrilateral cantilever beam structure with four resistive sensors, each sputtered with a different sensitive material, including indium oxide (<inline-formula><tex-math>$mathrm{In_{2}O_{3}}$</tex-math></inline-formula>) doped with Au, Ag, Pt, and Pd. The BOM consists of a bionic olfactory receptor layer and a bionic olfactory bulb layer, capable of encoding sensor signals and efficiently extracting odor features without manual feature engineering. This system focuses on the classification of food types based on odor characteristics. To verify the performance of the system, data collection and performance analysis were performed on seven kinds of fruits (apple, banana, orange, mango, strawberry, pear, kiwi). The proposed model can directly extract odor features from sensor signals without feature engineering. Compared with traditional method, the system achieves an improvement in classification accuracy from 78.1% to 91.9% when using the k-nearest neighbors classifier.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871084","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

Hybrid LPF-LSTM Model for Enhanced Epileptic Seizure Detection in EEG Signals 基于LPF-LSTM混合模型的脑电信号癫痫发作增强检测

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-07 DOI: 10.1109/LSENS.2025.3558422

Vaddi Venkata Narayana;Prakash Kodali

引用次数: 0

High-Efficiency and Compact RF Rectifier Design for Wireless Sensors in Extreme Environments 用于极端环境下无线传感器的高效紧凑射频整流器设计

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-03 DOI: 10.1109/LSENS.2025.3557455

Changzhen Liao;Guanghua Liu;Huaijin Zhang;Guozheng Zhao;Tao Jiang

{"title":"High-Efficiency and Compact RF Rectifier Design for Wireless Sensors in Extreme Environments","authors":"Changzhen Liao;Guanghua Liu;Huaijin Zhang;Guozheng Zhao;Tao Jiang","doi":"10.1109/LSENS.2025.3557455","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3557455","url":null,"abstract":"Radio frequency (RF) energy harvesting garners significant attention for prolonging the lifespan of wireless sensor networks (WSNs). However, its deployment in extreme environments is constrained by low RF power. Therefore, to guarantee the sustained functionality of WSNs, it is imperative to enhance rectification efficiency at low incident power. This letter proposes a novel rectifier with highly efficient operation at low incident power. The rectifier comprises a T-type power divider with a <inline-formula><tex-math>${CLC}$</tex-math></inline-formula> <inline-formula><tex-math>$pi$</tex-math></inline-formula> network and two identical subrectifiers. Utilizing this, the reflected power from the subrectifiers can be reinjected into the rectifier so that it can be reused, and the rectification efficiency can be improved. Theoretical analysis and performance comparison are carried out. The experimental results indicate that the proposed rectifier is capable of achieving high rectification efficiency at low incident power. The recorded efficiency remains notably above 10% at an incident power of −<inline-formula><tex-math>$ 30text{ dBm}$</tex-math></inline-formula>, with an operating bandwidth of up to <inline-formula><tex-math>$ 320text{ MHz}$</tex-math></inline-formula>.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871057","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