Pub Date : 2025-11-17DOI: 10.1109/LSENS.2025.3633315
Subham Koley;Sunil K Khijwania
This research aims to develop simple and novel optical fiber relative humidity (RH) sensor that employs intensity modulation via evanescent wave (EW) absorption. Proposed sensor exploits Al2O3/GO nanocomposite doped thin film of nanostructured silica as the sensing cladding on a centrally decladded plastic-clad silica (PCS) fiber. This configuration is used for the first time, to the best of the author's knowledge, for the development of optical fiber RH sensor. Comprehensive experimental investigations are carried out to establish response characteristics of the sensor. Proposed sensor demonstrates a significantly enhanced sensitivity of 0.0107 RH-1 while responding linearly over a dynamic range of 14%–86% RH. In addition, fast response/recovery time, excellent reversibility, repeatability, and reliability characteristics of the sensor make it suitable for real-field applications.
{"title":"Novel Al2O3/GO Nanocomposite-Based Highly Sensitive Optical Fiber Humidity Sensor","authors":"Subham Koley;Sunil K Khijwania","doi":"10.1109/LSENS.2025.3633315","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3633315","url":null,"abstract":"This research aims to develop simple and novel optical fiber relative humidity (RH) sensor that employs intensity modulation via evanescent wave (EW) absorption. Proposed sensor exploits Al<sub>2</sub>O<sub>3</sub>/GO nanocomposite doped thin film of nanostructured silica as the sensing cladding on a centrally decladded plastic-clad silica (PCS) fiber. This configuration is used for the first time, to the best of the author's knowledge, for the development of optical fiber RH sensor. Comprehensive experimental investigations are carried out to establish response characteristics of the sensor. Proposed sensor demonstrates a significantly enhanced sensitivity of 0.0107 RH<sup>-1</sup> while responding linearly over a dynamic range of 14%–86% RH. In addition, fast response/recovery time, excellent reversibility, repeatability, and reliability characteristics of the sensor make it suitable for real-field applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729303","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 : 2025-11-13DOI: 10.1109/LSENS.2025.3629702
Siyang Liu;Zijie Chen;Yiming Gao;Junrui Liang
People counting constitutes a crucial application of Internet of Things (IoT) technology. It offers valuable information for crowd management, security, and public health purposes. However, the majority of the current people counting sensors are powered either by batteries or by mains electricity. These power sources involve intricate installation procedures that frequently necessitate redecoration and arduous maintenance. This letter introduces a novel battery-free wireless floor tile sensor system for people counting. The floor tile terminal is composed of four quasi-static-toggling electromagnetic motion-powered switches. The foot traffic data transmitted are received by a gateway and subsequently forwarded to a cloud platform for analysis. The battery-free wireless floor tile is convenient to install. The entire system is capable of monitoring the number of people and their flow direction in real time. A prototype system is manufactured and installed at the entrance of the authors' laboratory for a field test. It achieves a 94.8% accuracy in walking directional identification and people counting. It is energy autonomy, low cost, and easy deployment. This study establishes a sustainable model for long-term indoor occupancy monitoring and crowd management. The design aligns with the current trend of eco-friendly, battery-free ambient IoT.
{"title":"Battery-Free Wireless Floor Tile for People Counting","authors":"Siyang Liu;Zijie Chen;Yiming Gao;Junrui Liang","doi":"10.1109/LSENS.2025.3629702","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3629702","url":null,"abstract":"People counting constitutes a crucial application of Internet of Things (IoT) technology. It offers valuable information for crowd management, security, and public health purposes. However, the majority of the current people counting sensors are powered either by batteries or by mains electricity. These power sources involve intricate installation procedures that frequently necessitate redecoration and arduous maintenance. This letter introduces a novel battery-free wireless floor tile sensor system for people counting. The floor tile terminal is composed of four quasi-static-toggling electromagnetic motion-powered switches. The foot traffic data transmitted are received by a gateway and subsequently forwarded to a cloud platform for analysis. The battery-free wireless floor tile is convenient to install. The entire system is capable of monitoring the number of people and their flow direction in real time. A prototype system is manufactured and installed at the entrance of the authors' laboratory for a field test. It achieves a 94.8% accuracy in walking directional identification and people counting. It is energy autonomy, low cost, and easy deployment. This study establishes a sustainable model for long-term indoor occupancy monitoring and crowd management. The design aligns with the current trend of eco-friendly, battery-free ambient IoT.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560704","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 : 2025-11-12DOI: 10.1109/LSENS.2025.3632112
Vinicius de Carvalho;Andre Lazzaretti;Marcia Muller;José Luís Fabris
This work presents the monitoring of torsion in a flexible cylindrical structure instrumented with a helically wound optical fiber. The sensing element consists of a standard fiber embedded in elastomer, forming a macrobend-based structure. Controlled angular displacements from $-90^{circ }$ to $90^{circ }$ were applied by twisting the structure. Distinct torsional states produced differentiable transmission spectra, with counterclockwise torsion increasing and clockwise torsion decreasing the mean transmittance across 475–750 nm. Single-wavelength fits showed wavelength-dependent behavior and limited predictive accuracy, highlighting the advantages of multivariate approaches that use full-spectrum information. Multivariate regression models were trained on spectral data reduced by principal component analysis for torsion prediction, with the elastic net achieving the best performance ($R^{2} = 0.99$). Residual analysis showed that 95% of prediction errors were below $3.5^{circ }$ for the 15-cm-long structure. These results confirm the feasibility of the proposed method for torsion sensing in soft robotic devices.
{"title":"Torsion Monitoring With a Helically Wound Macrobend Optical Fiber Sensor","authors":"Vinicius de Carvalho;Andre Lazzaretti;Marcia Muller;José Luís Fabris","doi":"10.1109/LSENS.2025.3632112","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3632112","url":null,"abstract":"This work presents the monitoring of torsion in a flexible cylindrical structure instrumented with a helically wound optical fiber. The sensing element consists of a standard fiber embedded in elastomer, forming a macrobend-based structure. Controlled angular displacements from <inline-formula><tex-math>$-90^{circ }$</tex-math></inline-formula> to <inline-formula><tex-math>$90^{circ }$</tex-math></inline-formula> were applied by twisting the structure. Distinct torsional states produced differentiable transmission spectra, with counterclockwise torsion increasing and clockwise torsion decreasing the mean transmittance across 475–750 nm. Single-wavelength fits showed wavelength-dependent behavior and limited predictive accuracy, highlighting the advantages of multivariate approaches that use full-spectrum information. Multivariate regression models were trained on spectral data reduced by principal component analysis for torsion prediction, with the elastic net achieving the best performance (<inline-formula><tex-math>$R^{2} = 0.99$</tex-math></inline-formula>). Residual analysis showed that 95% of prediction errors were below <inline-formula><tex-math>$3.5^{circ }$</tex-math></inline-formula> for the 15-cm-long structure. These results confirm the feasibility of the proposed method for torsion sensing in soft robotic devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674710","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 : 2025-11-07DOI: 10.1109/LSENS.2025.3630197
Ebrahim Nehary;Sreeraman Rajan
Phonocardiogram (PCG) can be used to detect cardiac conditions and support the initial diagnosis of cardiovascular disease, a critical health issue that requires early detection to allow timely treatment and potentially save lives. Classification of PCG signals as normal or abnormal is currently done using learning algorithms which require homogeneous training data. However, PCG datasets are often collected using stethoscopes with varying characteristics, from different individuals, and in diverse controlled or uncontrolled environments. This results in dataset heterogeneity, which poses a challenge for training effective deep learning models. This study explores the recently proposed Kolmogorov–Arnold Networks (KANs), which incorporate different trainable function families such as splines and wavelets for the classification of PCG and evaluate their robustness against data heterogeneity. KAN is compared with a traditional Multilayer Perceptron (MLP) on heterogeneous and homogeneous PCG datasets to determine the most suitable model for PCG classification. Experimental results show that KAN with wavelet-based functions outperforms KAN with spline functions and MLP on both datasets, achieving superior performance with parameters and computational costs comparable to those of MLP. In contrast, the spline-based KAN performs well on homogeneous data but poorly on heterogeneous data, incurring the highest computational cost and model complexity. KAN with wavelet functions outperforms MLP by over 10% in most cases and outperforms state-of-the art methods. In summary, KAN with wavelet functions demonstrate strong performance across dataset types and may be a promising candidate for fully connected layers in deep learning models, irrespective of whether the dataset is homogeneous or heterogeneous.
{"title":"Phonocardiogram Classification Model With Kolmogorov–Arnold Network for Training With Heterogeneous Dataset","authors":"Ebrahim Nehary;Sreeraman Rajan","doi":"10.1109/LSENS.2025.3630197","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3630197","url":null,"abstract":"Phonocardiogram (PCG) can be used to detect cardiac conditions and support the initial diagnosis of cardiovascular disease, a critical health issue that requires early detection to allow timely treatment and potentially save lives. Classification of PCG signals as normal or abnormal is currently done using learning algorithms which require homogeneous training data. However, PCG datasets are often collected using stethoscopes with varying characteristics, from different individuals, and in diverse controlled or uncontrolled environments. This results in dataset heterogeneity, which poses a challenge for training effective deep learning models. This study explores the recently proposed Kolmogorov–Arnold Networks (KANs), which incorporate different trainable function families such as splines and wavelets for the classification of PCG and evaluate their robustness against data heterogeneity. KAN is compared with a traditional Multilayer Perceptron (MLP) on heterogeneous and homogeneous PCG datasets to determine the most suitable model for PCG classification. Experimental results show that KAN with wavelet-based functions outperforms KAN with spline functions and MLP on both datasets, achieving superior performance with parameters and computational costs comparable to those of MLP. In contrast, the spline-based KAN performs well on homogeneous data but poorly on heterogeneous data, incurring the highest computational cost and model complexity. KAN with wavelet functions outperforms MLP by over 10% in most cases and outperforms state-of-the art methods. In summary, KAN with wavelet functions demonstrate strong performance across dataset types and may be a promising candidate for fully connected layers in deep learning models, irrespective of whether the dataset is homogeneous or heterogeneous.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674715","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 : 2025-11-07DOI: 10.1109/LSENS.2025.3630485
Mohannad K Sabir;Bashar S. Falih;Łukasz Gierz;Aymen Saad;Mohammed Ahmed Subhi;Montadar Abas Taher
European bee-eaters (Merops genus) pose significant challenges to beekeepers by preying on worker bees, reducing hive productivity. In this letter, a new approach for European bee-eater sound recognition employing convolutional neural networks (CNNs) based on classically trained classification models is presented. The short-time Fourier transform computes the time–frequency representation of the bird sounds, which acts as input to CNNs. The precision of the classifier was confirmed over 1000 spectrogram images per bird species and done on 11 families. The proposed method obtained 98.45% accuracy for the 11 bird species and 100% for identifying bee-eater sounds. The resultant algorithm could be applied on a small, minicomputer type of device such as Raspberry Pi, with an incorporated frightening function for beekeepers, which helps in preserving their hives and harvesting more honey.
{"title":"Deep and Machine Learning-Based Detection of European Bee-Eaters Using Bird Sounds","authors":"Mohannad K Sabir;Bashar S. Falih;Łukasz Gierz;Aymen Saad;Mohammed Ahmed Subhi;Montadar Abas Taher","doi":"10.1109/LSENS.2025.3630485","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3630485","url":null,"abstract":"European bee-eaters (<italic>Merops</i> genus) pose significant challenges to beekeepers by preying on worker bees, reducing hive productivity. In this letter, a new approach for European bee-eater sound recognition employing convolutional neural networks (CNNs) based on classically trained classification models is presented. The short-time Fourier transform computes the time–frequency representation of the bird sounds, which acts as input to CNNs. The precision of the classifier was confirmed over 1000 spectrogram images per bird species and done on 11 families. The proposed method obtained 98.45% accuracy for the 11 bird species and 100% for identifying bee-eater sounds. The resultant algorithm could be applied on a small, minicomputer type of device such as Raspberry Pi, with an incorporated frightening function for beekeepers, which helps in preserving their hives and harvesting more honey.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11234890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082226","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}
Pub Date : 2025-11-06DOI: 10.1109/LSENS.2025.3630120
Pi-Yun Chen;Chun-Yu Lin;Ping-Tzan Huang;Neng-Sheng Pai;Chao-Lin Kuo;Chien-Ming Li;Chia-Hung Lin
Clinical assessment methods for Parkinson's disease (PD) commonly rely on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale and the Health-Related Quality of Life questionnaire. Both methods employ structured question-and-answer assessments to evaluate the severity and progression of patients with related PD by assessing the nonmotor and motor experiences, movement disorders, and motor complications, along with complications of therapy. However, these methods need face-to-face interaction and are time-consuming (typically taking >20 min). Moreover, the assessment outcomes are often influenced by the clinician's expertise and subjective judgments. In addition, these methods also lack the capability to objectively and automatically quantify both tremor severity level and tremor classification in PD patients. To overcome the aforementioned limitations, this letter intends to implement a W-band (76–81 GHz) millimeter-wave-based noncontact biosensor that extracts the echo features for upper limb tremor classification. A deep learning method, cascade convolutional neural network-based classifier with combined feature extraction and pattern recognition tasks, is employed to identify tremor feature patterns for distinguishing typical tremor frequencies among low-frequency (<4.0 Hz), medium-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors through short-range (<1.0 m) and noncontact measurements.
{"title":"W-Band Millimeter-Wave Echo Features Detection With Cascade CNN-Based Classifier for Parkinson's Disease Tremors Classification","authors":"Pi-Yun Chen;Chun-Yu Lin;Ping-Tzan Huang;Neng-Sheng Pai;Chao-Lin Kuo;Chien-Ming Li;Chia-Hung Lin","doi":"10.1109/LSENS.2025.3630120","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3630120","url":null,"abstract":"Clinical assessment methods for Parkinson's disease (PD) commonly rely on the Movement Disorder Society<bold>-</b>Unified Parkinson's Disease Rating Scale and the Health-Related Quality of Life questionnaire. Both methods employ structured question-and-answer assessments to evaluate the severity and progression of patients with related PD by assessing the nonmotor and motor experiences, movement disorders, and motor complications, along with complications of therapy. However, these methods need face-to-face interaction and are time-consuming (typically taking >20 min). Moreover, the assessment outcomes are often influenced by the clinician's expertise and subjective judgments. In addition, these methods also lack the capability to objectively and automatically quantify both tremor severity level and tremor classification in PD patients. To overcome the aforementioned limitations, this letter intends to implement a W-band (76–81 GHz) millimeter-wave-based noncontact biosensor that extracts the echo features for upper limb tremor classification. A deep learning method, cascade convolutional neural network-based classifier with combined feature extraction and pattern recognition tasks, is employed to identify tremor feature patterns for distinguishing typical tremor frequencies among low-frequency (<italic><</i>4.0 Hz), medium-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors through short-range (<1.0 m) and noncontact measurements.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560707","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}
Most commercial optocouplers integrate at least two optical elements, such as a light-emitting diode (LED) and a silicon photodiode (PD), which function as a simple signal switch. However, this hybrid approach not only makes high-level device integration difficult but also increases fabrication complexity and decreases reliability. To achieve a compact, high-performance optocoupler, this study integrates an LED and a PD on a sapphire-based gallium nitride (GaN) epi-wafer into a single chip using a monolithic microfabrication process. The design involves patterning an annular interdigitated microstructure in which the LED is surrounded by the PD. This method is suitable for batch fabrication and enhances coupling efficiency by enlarging the active area via the annular and interdigitated structures. Measurement results revealed that the proposed chip with annular interdigitated structures generated a photocurrent of 0.176 mA when an 80 mA current was applied to the emitting element. A high current transfer ratio of 0.23% was achieved, indicating excellent performance. In addition, the proposed optocoupler requires fewer PDs, thereby reducing chip size and simplifying packaging.
{"title":"An Improved Monolithic GaN-Based Optocoupler With Annular Interdigitated Microstructures","authors":"Jhihfong Liou;Yuwei Chen;Huiqi Xie;Shengyung Wang;Chengshiun Liou;Chingfu Tsou","doi":"10.1109/LSENS.2025.3629077","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3629077","url":null,"abstract":"Most commercial optocouplers integrate at least two optical elements, such as a light-emitting diode (LED) and a silicon photodiode (PD), which function as a simple signal switch. However, this hybrid approach not only makes high-level device integration difficult but also increases fabrication complexity and decreases reliability. To achieve a compact, high-performance optocoupler, this study integrates an LED and a PD on a sapphire-based gallium nitride (GaN) epi-wafer into a single chip using a monolithic microfabrication process. The design involves patterning an annular interdigitated microstructure in which the LED is surrounded by the PD. This method is suitable for batch fabrication and enhances coupling efficiency by enlarging the active area via the annular and interdigitated structures. Measurement results revealed that the proposed chip with annular interdigitated structures generated a photocurrent of 0.176 mA when an 80 mA current was applied to the emitting element. A high current transfer ratio of 0.23% was achieved, indicating excellent performance. In addition, the proposed optocoupler requires fewer PDs, thereby reducing chip size and simplifying packaging.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560701","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 : 2025-11-03DOI: 10.1109/LSENS.2025.3627849
Felipe Hornung;Walter O. C. Flores;Katia Christina Zuffellato-Ribas;André Eugenio Lazzaretti;Marcia Muller;José Luís Fabris
Nanotechnology has been increasingly applied in agriculture to optimize crop performance. By combining irrigation with nanoparticles and appropriate lighting, plant development can be improved. This work shows that when the lighting overlaps the plasmonic resonances of silver and gold nanoparticles, tomato leaves exhibit higher chlorophyll content than under nonselective broadband lighting. Whereas chlorophyll can be quantified via destructive assays, the nondestructive method proposed in this work uses deep learning regression to estimate chlorophyll directly from reflectance spectroscopy of tomato leaves. This methodology avoids pigment extraction and tissue damage, being a more suitable tool for field applications. The deep neural network trained with leaf reflectance spectra from 400 to 800 nm achieved R$^{2}$ = 0.8925 for chlorophyll estimation. These findings can pave the way to increase crop yield, with optimized conditions through precision agriculture.
{"title":"Optical Sensing of Chlorophyll Content in Tomato Plants Exposed to Metal Nanoparticles Under Selective Lighting","authors":"Felipe Hornung;Walter O. C. Flores;Katia Christina Zuffellato-Ribas;André Eugenio Lazzaretti;Marcia Muller;José Luís Fabris","doi":"10.1109/LSENS.2025.3627849","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3627849","url":null,"abstract":"Nanotechnology has been increasingly applied in agriculture to optimize crop performance. By combining irrigation with nanoparticles and appropriate lighting, plant development can be improved. This work shows that when the lighting overlaps the plasmonic resonances of silver and gold nanoparticles, tomato leaves exhibit higher chlorophyll content than under nonselective broadband lighting. Whereas chlorophyll can be quantified via destructive assays, the nondestructive method proposed in this work uses deep learning regression to estimate chlorophyll directly from reflectance spectroscopy of tomato leaves. This methodology avoids pigment extraction and tissue damage, being a more suitable tool for field applications. The deep neural network trained with leaf reflectance spectra from 400 to 800 nm achieved R<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> = 0.8925 for chlorophyll estimation. These findings can pave the way to increase crop yield, with optimized conditions through precision agriculture.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510187","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 : 2025-10-31DOI: 10.1109/LSENS.2025.3627636
David V. Thiel;Anish Kumar;Krishnasamy T. Selvan;Hugo G. Espinosa
There has been significant global interest in the use of nonlethal methods to repel sharks during ocean-based activities. Given that sharks possess an electrosensory system for detecting prey, quasi-static electric fields were investigated as a potential wearable deterrent. A series of controlled experiments were conducted in a water tank (900 × 400 × 400 mm3) using a pulsed electric field (PEF) generator (5000 V at 8.5 kHz, 10 μs pulsewidth), with three conductivity values based on different salinity concentrations: 0.059, 0.149, and 1.042 S/m. The arc distance was approximately 1 mm, and the detector consisted of a germanium diode in parallel with a 130 kΩ resistor feeding a digital voltmeter. All equipment was battery-powered to minimize cable induction effects. The transmitter and receiver were enclosed in waterproof plastic bags under 40 mm of water. These data were fitted to a log–log power law (slope = −1.85, r2 = 0.96). The received voltage power law was less than the theoretical prediction from the geophysical resistivity method (slope = −3.0), likely due to side reflections in the water tank. Water conductivity had a minimal effect on the results, suggesting the findings are representative of saline water conditions. Given the small, portable, and insulated nature of the equipment, it is feasible to extrapolate the electric field strength at a distance in open water for potential shark-deterrent applications. Unlike permanent magnets, electric signals can be easily manipulated to minimize shark habituation.
{"title":"Toward an Electric Shark Deterrent: Electric Field Attenuation in Saline Water","authors":"David V. Thiel;Anish Kumar;Krishnasamy T. Selvan;Hugo G. Espinosa","doi":"10.1109/LSENS.2025.3627636","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3627636","url":null,"abstract":"There has been significant global interest in the use of nonlethal methods to repel sharks during ocean-based activities. Given that sharks possess an electrosensory system for detecting prey, quasi-static electric fields were investigated as a potential wearable deterrent. A series of controlled experiments were conducted in a water tank (900 × 400 × 400 mm<sup>3</sup>) using a pulsed electric field (PEF) generator (5000 V at 8.5 kHz, 10 μs pulsewidth), with three conductivity values based on different salinity concentrations: 0.059, 0.149, and 1.042 S/m. The arc distance was approximately 1 mm, and the detector consisted of a germanium diode in parallel with a 130 kΩ resistor feeding a digital voltmeter. All equipment was battery-powered to minimize cable induction effects. The transmitter and receiver were enclosed in waterproof plastic bags under 40 mm of water. These data were fitted to a log–log power law (slope = −1.85, <italic>r</i><sup>2</sup> = 0.96). The received voltage power law was less than the theoretical prediction from the geophysical resistivity method (slope = −3.0), likely due to side reflections in the water tank. Water conductivity had a minimal effect on the results, suggesting the findings are representative of saline water conditions. Given the small, portable, and insulated nature of the equipment, it is feasible to extrapolate the electric field strength at a distance in open water for potential shark-deterrent applications. Unlike permanent magnets, electric signals can be easily manipulated to minimize shark habituation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510186","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 : 2025-10-31DOI: 10.1109/LSENS.2025.3626751
Fangfang Zhang;Hao Sun;Jinzhu Peng;Haijing Wang
Millimeter-wave radar is widely used for indoor human activity recognition due to its privacy-preserving nature, with point cloud data effectively capturing target geometry. However, the sparsity and dynamic nature of these point clouds leads to unstable feature extraction, and resource constraints challenge large-scale neural network deployment. To address this, this letter proposes the Lightweight PointNet-BiLSTM with SE-Net (LPBS-Net), a lightweight network integrating a squeeze-and-excitation (SE) attention mechanism and bidirectional long short-term memory (BiLSTM) into a streamlined PointNet backbone, enhancing spatiotemporal feature modeling for dynamic point clouds. To overcome PointNet's need for fixed input point counts and its sensitivity to sparse distributions, we introduce Gaussian-based intensity and repeat padding, which selects base points by reflection intensity and uses Gaussian perturbation and repeated sampling to mitigate sparsity-induced feature degradation. Experiments on two public datasets show that LPBS-Net achieves 97.11% accuracy on the MMActivity dataset with only 0.176 M parameters, reducing model size by 84% compared to PointNet-BiLSTM, and outperforming existing methods, with maximum accuracy improvements exceeding 30%. The proposed lightweight network offers high accuracy and computational efficiency, evidenced by its low parameter count and floating point operations (FLOPs), making it suitable for deployment on resource-constrained edge devices.
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