Pub Date : 2025-01-08DOI: 10.1109/LSENS.2025.3527229
Ajay Beniwal;Rahul Gond;Xenofon Karagiorgis;Brajesh Rawat;Chong Li
In this letter, a room temperature (RT) (∼27 °C) operated ferric oxide/polyaniline (Fe2O3/PANI) composite-based flexible ammonia sensor with substantial sensing performance is reported. Initially, interdigitated electrodes were screen printed (using graphene-carbon-based ink) on a bio-degradable paper substrate. Further, PANI nanofibers were electrospun on printed IDEs, followed by drop casting a layer of Fe2O3. X-ray diffraction and Fourier transform infrared spectroscopy studies were performed to confirm the composite formation, followed by scanning electron microscopy analysis to examine the sensing surface morphology. The ammonia sensing performance was examined within the range of 0.5 ppm (i.e., 500 ppb) to 50 ppm, with a 1.99% response achieved even at 0.5 ppm. The response/recovery times were noted as 950/250 s toward 0.5 ppm of ammonia. In addition, selectivity toward interference gases including carbon dioxide, nitrogen dioxide, carbon monoxide, and sulfur dioxide was also investigated. The proposed sensing mechanism of the composite material toward ammonia gas detection is also presented.
{"title":"Room-Temperature-Operated Fe2O3/PANI-Based Flexible and Eco-Friendly Ammonia Sensor With Sub-ppm Detectability","authors":"Ajay Beniwal;Rahul Gond;Xenofon Karagiorgis;Brajesh Rawat;Chong Li","doi":"10.1109/LSENS.2025.3527229","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527229","url":null,"abstract":"In this letter, a room temperature (RT) (∼27 °C) operated ferric oxide/polyaniline (Fe<sub>2</sub>O<sub>3</sub>/PANI) composite-based flexible ammonia sensor with substantial sensing performance is reported. Initially, interdigitated electrodes were screen printed (using graphene-carbon-based ink) on a bio-degradable paper substrate. Further, PANI nanofibers were electrospun on printed IDEs, followed by drop casting a layer of Fe<sub>2</sub>O<sub>3</sub>. X-ray diffraction and Fourier transform infrared spectroscopy studies were performed to confirm the composite formation, followed by scanning electron microscopy analysis to examine the sensing surface morphology. The ammonia sensing performance was examined within the range of 0.5 ppm (i.e., 500 ppb) to 50 ppm, with a 1.99% response achieved even at 0.5 ppm. The response/recovery times were noted as 950/250 s toward 0.5 ppm of ammonia. In addition, selectivity toward interference gases including carbon dioxide, nitrogen dioxide, carbon monoxide, and sulfur dioxide was also investigated. The proposed sensing mechanism of the composite material toward ammonia gas detection is also presented.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993287","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}
Pub Date : 2025-01-08DOI: 10.1109/LSENS.2025.3527116
Jan Steckel;Pamela Rivera Parra;Arne Aerts;Dennis Laurijssen;Wouter Jansen;Walter Daems;Jesse Barber
In this letter, we introduce the flutter real-time imaging sonar (FL-RTIS): a novel sensor system that integrates a high-speed camera with a 3-D sonar sensor to investigate insect ensonification. By capturing and synchronizing high-resolution video with dense 3-D acoustic data, FL-RTIS provides a detailed analysis of the echo dynamics from fluttering insects. This multimodal approach allows for an unprecedented study of the acoustic interactions between bats and their prey, facilitating more profound insights into evolutionary adaptations in predator-prey dynamics. The capabilities of the FL-RTIS are demonstrated through laboratory experiments and field tests, highlighting its potential for gathering large datasets and showing the potential for new avenues to understanding complex biological interactions.
{"title":"FL-RTIS, a Novel Multimodal Sensor Using High-Speed Camera and Active 3-D Sonar for Insect Ensonification","authors":"Jan Steckel;Pamela Rivera Parra;Arne Aerts;Dennis Laurijssen;Wouter Jansen;Walter Daems;Jesse Barber","doi":"10.1109/LSENS.2025.3527116","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527116","url":null,"abstract":"In this letter, we introduce the flutter real-time imaging sonar (FL-RTIS): a novel sensor system that integrates a high-speed camera with a 3-D sonar sensor to investigate insect ensonification. By capturing and synchronizing high-resolution video with dense 3-D acoustic data, FL-RTIS provides a detailed analysis of the echo dynamics from fluttering insects. This multimodal approach allows for an unprecedented study of the acoustic interactions between bats and their prey, facilitating more profound insights into evolutionary adaptations in predator-prey dynamics. The capabilities of the FL-RTIS are demonstrated through laboratory experiments and field tests, highlighting its potential for gathering large datasets and showing the potential for new avenues to understanding complex biological interactions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993291","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}
Pub Date : 2025-01-07DOI: 10.1109/LSENS.2025.3526597
Eudald Sangenis;Andrei M. Shkel
The integration of virtual reality (VR) and augmented reality (AR) technologies into location-based services and applications necessitates precise navigation within an absolute geodetic reference frame, utilizing latitude, longitude, and altitude (LLA) coordinates. Typically, VR/AR headsets establish a local Cartesian (XYZ) world coordinate (WC) frame with an arbitrary initial origin and orientation. For accurate geodetic navigation, it is essential to align these devices to the true North (TN). This letter introduces an AR-based method for achieving the initial alignment of the WC frame relative to the geodetic frame. We developed an AR user interface to visually guide users to a known target with LLA coordinates, indirectly aligning the system to TN. Using a Magic Leap 2 AR headset, we evaluated our approach against traditional magnetometer-based methods. Our experimental results demonstrated that our method reduces the mean angular error by a factor of 4× and the standard deviation ($sigma$) by 5× compared to traditional magnetometer methods. This improvement can eliminate the need for initial magnetometer calibration, offering a more efficient and robust solution for TN alignment in AR/VR applications.
{"title":"Fast-Alignment of AR Headset From Local to Geodetic Coordinate Frame for Navigation and Mixed Reality Applications","authors":"Eudald Sangenis;Andrei M. Shkel","doi":"10.1109/LSENS.2025.3526597","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3526597","url":null,"abstract":"The integration of virtual reality (VR) and augmented reality (AR) technologies into location-based services and applications necessitates precise navigation within an absolute geodetic reference frame, utilizing latitude, longitude, and altitude (LLA) coordinates. Typically, VR/AR headsets establish a local Cartesian (XYZ) world coordinate (WC) frame with an arbitrary initial origin and orientation. For accurate geodetic navigation, it is essential to align these devices to the true North (TN). This letter introduces an AR-based method for achieving the initial alignment of the WC frame relative to the geodetic frame. We developed an AR user interface to visually guide users to a known target with LLA coordinates, indirectly aligning the system to TN. Using a Magic Leap 2 AR headset, we evaluated our approach against traditional magnetometer-based methods. Our experimental results demonstrated that our method reduces the mean angular error by a factor of 4× and the standard deviation (<inline-formula><tex-math>$sigma$</tex-math></inline-formula>) by 5× compared to traditional magnetometer methods. This improvement can eliminate the need for initial magnetometer calibration, offering a more efficient and robust solution for TN alignment in AR/VR applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993217","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}
Pub Date : 2025-01-06DOI: 10.1109/LSENS.2025.3526446
Abisek Dahal;Soumen Moulik
Human activity recognition (HAR) and classification is one of the most hyped and trending domains in the last decade. HAR involves multiple hit and trial approaches, machine and deep learning have emerged as excellent techniques for analyzing various physiological sensors used to capture human activities. This letter introduce a multistream convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) framework that works on physiological signals corresponding to different activities, in order to achieve an enhanced HAR system. In this work EMG signals that capture the muscles data during activities are used to classify various activities. We achieve an overall average of 98.06% accuracy in predicting activities. In addition to that we achieve 10%–20% more as compared to benchmark model in similar dataset with less computational time. Further the proposed model demonstrates better and remarkable performance in HAR eight-channel benchmark SOTA dataset.
{"title":"Multistream CNN-BiLSTM Framework for Enhanced Human Activity Recognition Leveraging Physiological Signal","authors":"Abisek Dahal;Soumen Moulik","doi":"10.1109/LSENS.2025.3526446","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3526446","url":null,"abstract":"Human activity recognition (HAR) and classification is one of the most hyped and trending domains in the last decade. HAR involves multiple hit and trial approaches, machine and deep learning have emerged as excellent techniques for analyzing various physiological sensors used to capture human activities. This letter introduce a multistream convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) framework that works on physiological signals corresponding to different activities, in order to achieve an enhanced HAR system. In this work EMG signals that capture the muscles data during activities are used to classify various activities. We achieve an overall average of <bold>98.06%</b> accuracy in predicting activities. In addition to that we achieve 10%–20% more as compared to benchmark model in similar dataset with less computational time. Further the proposed model demonstrates better and remarkable performance in HAR eight-channel benchmark SOTA dataset.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993218","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}
Pub Date : 2025-01-02DOI: 10.1109/LSENS.2025.3525485
Anish Prabhu;Aparajita Naik;Sakshi Raut;Narayan Vetrekar;Raghavendra Ramachandra;R. S. Gad
The demand for reliable methods to detect artificially degreened citrus fruits is growing in the agricultural sector. In this letter, we propose a spectral imaging-based approach to differentiate natural and artificially degreened lemons using eight narrow spectral bands within the visible and near-infrared range. To support this research, we introduce the Spectral Imaging Lemon database, consisting of 7168 images of natural and degreened lemons. Experiments were conducted across the wavelengths from 530 to 1000 nm, leveraging six feature descriptors and a support vector machine (SVM) classifier. The proposed method achieved an impressive 93.5% average classification accuracy, showcasing its effectiveness.
{"title":"Employing Nondestructive Approach of Spectral Imaging to Detect Artificially Degreened Lemon","authors":"Anish Prabhu;Aparajita Naik;Sakshi Raut;Narayan Vetrekar;Raghavendra Ramachandra;R. S. Gad","doi":"10.1109/LSENS.2025.3525485","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3525485","url":null,"abstract":"The demand for reliable methods to detect artificially degreened citrus fruits is growing in the agricultural sector. In this letter, we propose a spectral imaging-based approach to differentiate natural and artificially degreened lemons using eight narrow spectral bands within the visible and near-infrared range. To support this research, we introduce the Spectral Imaging Lemon database, consisting of 7168 images of natural and degreened lemons. Experiments were conducted across the wavelengths from 530 to 1000 nm, leveraging six feature descriptors and a support vector machine (SVM) classifier. The proposed method achieved an impressive 93.5% average classification accuracy, showcasing its effectiveness.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993374","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}
Pub Date : 2025-01-02DOI: 10.1109/LSENS.2024.3525127
Hui Bi;Weihao Xu;Shuang Jin;Jingjing Zhang
As an extension of synthetic aperture radar (SAR), SAR tomography (TomoSAR) technology can reduce the overlapping in 2-D SAR image and separate multiscatterer along the elevation direction, thereby achieving the high-precision 3-D reconstruction of the surveillance area. However, in practical spaceborne TomoSAR application, the quality of 3-D imaging is restricted by the limited number of baselines and their uneven distribution. Therefore, it is necessary to find advanced signal processing technology to achieve the target 3-D recovery when the amount of data is limited. In this letter, a novel Tikhonov regularization and Bayesian information criterion (BIC)-based nonparametric iterative adaptive approach (IAA), named RIAA-BIC, is proposed and introduced to the spaceborne data processing. Compared with conventional spectral estimation, compressed sensing-based, and IAA algorithms, the proposed method incorporates the Tikhonov regularization term to avoid the problem of solving nonlinear ill-posed equation in the elevation inversion. Furthermore, the BIC model selection tool can eliminate the false or weak scatterers, thereby improving the 3-D reconstruction accuracy of the surveillance area. Experimental results based on TerraSAR-X dataset verify the proposed method.
{"title":"Tomographic Inversion of Urban Area via Tikhonov Regularization and Bayesian Information Criterion","authors":"Hui Bi;Weihao Xu;Shuang Jin;Jingjing Zhang","doi":"10.1109/LSENS.2024.3525127","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3525127","url":null,"abstract":"As an extension of synthetic aperture radar (SAR), SAR tomography (TomoSAR) technology can reduce the overlapping in 2-D SAR image and separate multiscatterer along the elevation direction, thereby achieving the high-precision 3-D reconstruction of the surveillance area. However, in practical spaceborne TomoSAR application, the quality of 3-D imaging is restricted by the limited number of baselines and their uneven distribution. Therefore, it is necessary to find advanced signal processing technology to achieve the target 3-D recovery when the amount of data is limited. In this letter, a novel Tikhonov regularization and Bayesian information criterion (BIC)-based nonparametric iterative adaptive approach (IAA), named RIAA-BIC, is proposed and introduced to the spaceborne data processing. Compared with conventional spectral estimation, compressed sensing-based, and IAA algorithms, the proposed method incorporates the Tikhonov regularization term to avoid the problem of solving nonlinear ill-posed equation in the elevation inversion. Furthermore, the BIC model selection tool can eliminate the false or weak scatterers, thereby improving the 3-D reconstruction accuracy of the surveillance area. Experimental results based on TerraSAR-X dataset verify the proposed method.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993373","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}
Pub Date : 2024-12-30DOI: 10.1109/LSENS.2024.3523905
Yasuhiro Tanaka;Kazutaka Tateyama
This letter presents the feasibility of a classification that divides six surfaces into more than four surfaces using radiometric values retrieved from brightness temperatures (TBs) observed at 6- and 36-GHz radiometers with vertical (V) and horizontal (H) polarizations on asphalt surface. The feasibility was investigated by using the Mahalanobis-distance-based approach of the canonical discriminant analysis. Combining 6 V (or 36 V) and 36 H emissivities, with the use of the surface temperature, showed the classification accuracy of 94%. In addition, combining the polarization ratio at 36 GHz TBs (PR36) and the cross-polarized gradient ratio between 36 H and 6 V TBs (XGPR36H06V), without the use of the surface temperature, showed the classification accuracy of 97%. Both the combination of 6 V and 36 H emissivities and the combination of PR36 and XGPR36H06V have the potential for dividing six surface conditions into five surface conditions. Results suggest that the combination of PR36 and XGPR36H06V potentially has a classification ability similar to that of 6 V and 36 H emissivities.
{"title":"Analysis of Microwave Radiometry of Snow and Ice on an Outdoor Experimental Asphalt Surface","authors":"Yasuhiro Tanaka;Kazutaka Tateyama","doi":"10.1109/LSENS.2024.3523905","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3523905","url":null,"abstract":"This letter presents the feasibility of a classification that divides six surfaces into more than four surfaces using radiometric values retrieved from brightness temperatures (TBs) observed at 6- and 36-GHz radiometers with vertical (V) and horizontal (H) polarizations on asphalt surface. The feasibility was investigated by using the Mahalanobis-distance-based approach of the canonical discriminant analysis. Combining 6 V (or 36 V) and 36 H emissivities, with the use of the surface temperature, showed the classification accuracy of 94%. In addition, combining the polarization ratio at 36 GHz TBs (PR<sub>36</sub>) and the cross-polarized gradient ratio between 36 H and 6 V TBs (XGPR<sub>36H06V</sub>), without the use of the surface temperature, showed the classification accuracy of 97%. Both the combination of 6 V and 36 H emissivities and the combination of PR<sub>36</sub> and XGPR<sub>36H06V</sub> have the potential for dividing six surface conditions into five surface conditions. Results suggest that the combination of PR<sub>36</sub> and XGPR<sub>36H06V</sub> potentially has a classification ability similar to that of 6 V and 36 H emissivities.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993290","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}
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