Pub Date : 2026-01-16DOI: 10.1109/LSENS.2026.3654985
Riya Rani S S;Sumit Datta
Robust detection of underwater targets by passive sonar is hindered by weak tonal emissions masked by strong ambient noise in the marine environment. These narrowband components, represented as spectral lines in the low frequency analysis and recording (LOFAR) spectrum of received acoustic signals, are critical for target recognition but remain difficult to enhance under low signal-to-noise ratio (SNR) conditions. We propose a spectral enhancement framework that combines a frequency domain adaptive line enhancer (ALE) with a dual-attention-guided wavelet domain fast iterative shrinkage thresholding algorithm network (FISTA-Net). The ALE stage suppresses broadband noise while preserving spectral structures, whereas wavelet domain FISTA employs multiresolution analysis and attention-guided weighting to enhance weak spectral features under sparsity constraints. Experimental results show an average SNR improvement of 4.87 dB over block-processed sparsity-based on ALE and 6.37 dB over conventional ALE. To further study the recognition accuracy of the proposed method, a common ResNet-based classifier is used. The proposed method outperforms existing approaches, achieving higher classification accuracy and demonstrating its effectiveness for underwater target detection and recognition.
{"title":"Spectral Line Enhancement for Noise-Resilient Passive Sonar Detection Using Dual-Attention-Guided Wavelet Domain FISTA-Net","authors":"Riya Rani S S;Sumit Datta","doi":"10.1109/LSENS.2026.3654985","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3654985","url":null,"abstract":"Robust detection of underwater targets by passive sonar is hindered by weak tonal emissions masked by strong ambient noise in the marine environment. These narrowband components, represented as spectral lines in the low frequency analysis and recording (LOFAR) spectrum of received acoustic signals, are critical for target recognition but remain difficult to enhance under low signal-to-noise ratio (SNR) conditions. We propose a spectral enhancement framework that combines a frequency domain adaptive line enhancer (ALE) with a dual-attention-guided wavelet domain fast iterative shrinkage thresholding algorithm network (FISTA-Net). The ALE stage suppresses broadband noise while preserving spectral structures, whereas wavelet domain FISTA employs multiresolution analysis and attention-guided weighting to enhance weak spectral features under sparsity constraints. Experimental results show an average SNR improvement of 4.87 dB over block-processed sparsity-based on ALE and 6.37 dB over conventional ALE. To further study the recognition accuracy of the proposed method, a common ResNet-based classifier is used. The proposed method outperforms existing approaches, achieving higher classification accuracy and demonstrating its effectiveness for underwater target detection and recognition.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175652","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}
This letter investigates the origin of zero-rate-output (ZRO) anomalies observed in Lissajous frequcy-modulated (LFM) microelectromechanical systems (MEMS) gyroscopes employing electrostatic ring resonators. Although LFM detection offers high scale-factor stability through frequency-based angular-rate encoding, residual bias errors often emerge even under zero angular rate. Experimental measurements reveal the presence of a second-harmonic component in the frequency modulation that cannot be explained by conventional linear cross-axis coupling models. To clarify this behavior, a theoretical model is developed considering the inclination of parallel-plate electrostatic transducers under large-amplitude wineglass-mode vibration. The analysis shows that the electrostatic stiffness becomes quadratically dependent on the vibration amplitude of the orthogonal mode, producing a second-order coupling term proportional to $cos (2theta)$. Experimental results obtained using an field-programmable gate array (FPGA)-based dual-axis control system confirm that the amplitude of the second-harmonic component scales with the square of the drive voltage, validating the proposed model. These findings demonstrate that plate-tilt-induced nonlinear coupling is a major source of ZRO fluctuation in LFM gyroscopes.
{"title":"Zero-Rate-Output Anomaly in Lissajous-FM Gyroscopes With Electrostatic Ring Resonators","authors":"Takashiro Tsukamoto;Roman Forke;Sebastian Weidlich;Daniel Bülz;Alexey Shaporin;Karla Hiller;Shuji Tanaka","doi":"10.1109/LSENS.2026.3654967","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3654967","url":null,"abstract":"This letter investigates the origin of zero-rate-output (ZRO) anomalies observed in Lissajous frequcy-modulated (LFM) microelectromechanical systems (MEMS) gyroscopes employing electrostatic ring resonators. Although LFM detection offers high scale-factor stability through frequency-based angular-rate encoding, residual bias errors often emerge even under zero angular rate. Experimental measurements reveal the presence of a second-harmonic component in the frequency modulation that cannot be explained by conventional linear cross-axis coupling models. To clarify this behavior, a theoretical model is developed considering the inclination of parallel-plate electrostatic transducers under large-amplitude wineglass-mode vibration. The analysis shows that the electrostatic stiffness becomes quadratically dependent on the vibration amplitude of the orthogonal mode, producing a second-order coupling term proportional to <inline-formula><tex-math>$cos (2theta)$</tex-math></inline-formula>. Experimental results obtained using an field-programmable gate array (FPGA)-based dual-axis control system confirm that the amplitude of the second-harmonic component scales with the square of the drive voltage, validating the proposed model. These findings demonstrate that plate-tilt-induced nonlinear coupling is a major source of ZRO fluctuation in LFM gyroscopes.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175669","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 : 2026-01-15DOI: 10.1109/LSENS.2026.3654361
R. C. Ajay Krishna;Banibrata Mukherjee
In this work, a robust and improved loosely coupled (LC) Global Navigation Satellite System (GNSS)- Inertial Navigation System (INS) integration scheme incorporating three important features, such as dynamic inertial measurement unit (IMU) calibration, Mahalanobis distance-based outlier rejection (MDOR) mechanism, and innovation-based adaptive estimation (IAE) technique, is presented for reliable and accurate navigation. Unscented Kalman filter (UKF)-based dynamic calibration of IMU is adapted here because it accurately transmits statistical distributions without linearization, which is better at managing nonlinear INS dynamics than the extended Kalman filter. Further, MDOR mechanism is proposed to identify and exclude erroneous GNSS measurements before the filter update, whereas, IAE technique is proposed to dynamically tune the filter’s noise covariance. A hardware setup is developed using a GNSS receiver, IMU sensor, and microcontroller to capture data for real vehicular trajectories. The proposed framework has been implemented in MATLAB and further experimentally demonstrated with real trajectory data. The navigation accuracy of the proposed method exhibits upto 75% improvement with respect to conventional LC integration. The contribution lies on careful integration and validation of dual-layer architecture with interlayer feedback mechanism and nested-architecture for known UKF-based IMU calibration. The proposed framework can provide a precise navigation solution to improve resilience even in partial GNSS challenging areas.
{"title":"Novel GNSS-INS Integration Scheme With UKF-Based Dynamic IMU Calibration and Dual-layer Design for Reliable Navigation","authors":"R. C. Ajay Krishna;Banibrata Mukherjee","doi":"10.1109/LSENS.2026.3654361","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3654361","url":null,"abstract":"In this work, a robust and improved loosely coupled (LC) Global Navigation Satellite System (GNSS)- Inertial Navigation System (INS) integration scheme incorporating three important features, such as dynamic inertial measurement unit (IMU) calibration, Mahalanobis distance-based outlier rejection (MDOR) mechanism, and innovation-based adaptive estimation (IAE) technique, is presented for reliable and accurate navigation. Unscented Kalman filter (UKF)-based dynamic calibration of IMU is adapted here because it accurately transmits statistical distributions without linearization, which is better at managing nonlinear INS dynamics than the extended Kalman filter. Further, MDOR mechanism is proposed to identify and exclude erroneous GNSS measurements before the filter update, whereas, IAE technique is proposed to dynamically tune the filter’s noise covariance. A hardware setup is developed using a GNSS receiver, IMU sensor, and microcontroller to capture data for real vehicular trajectories. The proposed framework has been implemented in MATLAB and further experimentally demonstrated with real trajectory data. The navigation accuracy of the proposed method exhibits upto 75% improvement with respect to conventional LC integration. The contribution lies on careful integration and validation of dual-layer architecture with interlayer feedback mechanism and nested-architecture for known UKF-based IMU calibration. The proposed framework can provide a precise navigation solution to improve resilience even in partial GNSS challenging areas.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082141","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 : 2026-01-15DOI: 10.1109/LSENS.2026.3654225
Da Xu;Zhenghao Lu;Zheng Shi;Xiaopeng Yu
A CMOS temperature sensor targeting automotive and industrial applications is presented. The sensor integrates a BJT-based sensing frontend with a second-order $Sigma Delta$ ADC. To address the accumulation of common-mode error in the integrator under low supply voltages, which can lead to large input common-mode deviations that reduce the integrator amplifier gain and degrade the ADC SNR, a novel sampling scheme is proposed. By means of a carefully designed sampling sequence, the proposed scheme maintains the amplifier input common-mode voltage within a small and predictable range, thereby stabilizing the amplifier gain and preventing SNR degradation. In addition, the sampling scheme reduces the number of ADC input branches, which effectively minimizes leakage current. To further enhance the measurement accuracy, a finite BJT current-gain compensation resistor and a bitstream-controlled dynamic element matching (BSC-DEM) technique are employed in the sensing frontend. Fabricated in a 180 nm CMOS process, the prototype achieves an inaccuracy of $pm$1.0 °C (3$sigma$) from −50 °C to 150 °C. The sensor consumes 6.3 μA from a 1.8 V supply at room temperature, achieves a resolution of 0.018 °C, and occupies an active area of 0.1 mm$^{2}$.
{"title":"A Low-Power BJT-Based CMOS Temperature Sensor Using a Common-Mode Error Suppression Sampling Scheme From −50 °C to 150 °C","authors":"Da Xu;Zhenghao Lu;Zheng Shi;Xiaopeng Yu","doi":"10.1109/LSENS.2026.3654225","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3654225","url":null,"abstract":"A CMOS temperature sensor targeting automotive and industrial applications is presented. The sensor integrates a BJT-based sensing frontend with a second-order <inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula> ADC. To address the accumulation of common-mode error in the integrator under low supply voltages, which can lead to large input common-mode deviations that reduce the integrator amplifier gain and degrade the ADC SNR, a novel sampling scheme is proposed. By means of a carefully designed sampling sequence, the proposed scheme maintains the amplifier input common-mode voltage within a small and predictable range, thereby stabilizing the amplifier gain and preventing SNR degradation. In addition, the sampling scheme reduces the number of ADC input branches, which effectively minimizes leakage current. To further enhance the measurement accuracy, a finite BJT current-gain compensation resistor and a bitstream-controlled dynamic element matching (BSC-DEM) technique are employed in the sensing frontend. Fabricated in a 180 nm CMOS process, the prototype achieves an inaccuracy of <inline-formula><tex-math>$pm$</tex-math></inline-formula>1.0 °C (3<inline-formula><tex-math>$sigma$</tex-math></inline-formula>) from −50 °C to 150 °C. The sensor consumes 6.3 μA from a 1.8 V supply at room temperature, achieves a resolution of 0.018 °C, and occupies an active area of 0.1 mm<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082248","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 : 2026-01-12DOI: 10.1109/LSENS.2026.3651748
Samprit Bose;Agnesh Chandra Yadav;Maheshkumar H. Kolekar
Low-light images often suffer from poor contrast, reduced visibility, and detail loss, with artifacts and distortions under complex lighting further complicating enhancement. To address these challenges, we introduce DARF-Net, a Retinex-inspired framework that separates input images into illumination and reflectance components for targeted enhancement. Our method, which uses an illumination-guided multihead attention module as the generator of a generative adversarial network, improves the illumination map, while a variational autoencoder supplemented with a spatial attention module improves the reflectance map. Together, the two attention modules improve brightness, structure, and overall visual quality. Comprehensive tests on the LOL and SICE datasets show that DARF-Net outperforms the state-of-the-art techniques in terms of peak signal-to-noise ratio, structural similarity index, and learned perceptual image patch similarity metrics while retaining computational efficiency.
{"title":"DARF-Net: A Dual Attention Retinex-Based Fusion Network for Low-Light Image Enhancement","authors":"Samprit Bose;Agnesh Chandra Yadav;Maheshkumar H. Kolekar","doi":"10.1109/LSENS.2026.3651748","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3651748","url":null,"abstract":"Low-light images often suffer from poor contrast, reduced visibility, and detail loss, with artifacts and distortions under complex lighting further complicating enhancement. To address these challenges, we introduce DARF-Net, a Retinex-inspired framework that separates input images into illumination and reflectance components for targeted enhancement. Our method, which uses an illumination-guided multihead attention module as the generator of a generative adversarial network, improves the illumination map, while a variational autoencoder supplemented with a spatial attention module improves the reflectance map. Together, the two attention modules improve brightness, structure, and overall visual quality. Comprehensive tests on the LOL and SICE datasets show that DARF-Net outperforms the state-of-the-art techniques in terms of peak signal-to-noise ratio, structural similarity index, and learned perceptual image patch similarity metrics while retaining computational efficiency.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175804","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 : 2026-01-12DOI: 10.1109/LSENS.2026.3651315
Vinothini S;Punitha N;Karthick P A;Ramakrishnan S
Pregnancy involves various anatomical and physiological changes, with weak and inefficient uterine contractions during early stages. As labor approaches, uterine activity becomes more coordinated due to enhanced synchronization of electrical signals. Monitoring this progression is critical for improving maternal and fetal health through early detection of complications and timely interventions. Uterine electromyography (uEMG) is a noninvasive technique for assessing uterine electrical activity and has emerged as a promising tool for tracking pregnancy progression. This study aims to evaluate synchronization measures derived from multichannel uEMG signals to characterize pregnancy progression. uEMG signals obtained from the publicly available databases are considered during before (T1) and after (T2) 26 weeks of gestation from term delivery. Signals from three bipolar channels, with a fourth derived channel, are preprocessed using a four-pole Butterworth bandpass filter (0.3–3 Hz) to mitigate noise. Propagation features, including maximum cross-correlation, mean coherence, peak coherence, imaginary coherence, and phase locking value (PLV), are extracted across all channel pairs. Random forest (RF)-based feature importance ranking is employed for feature selection, and machine learning classifiers, such as RF, adaptive boosting, and support vector machine, are used for classification. Results show that propagation features are able to characterize the pregnancy progression. Coherence-based measures decrease with increasing gestation, whereas PLV consistently increases in both intersubject and intrasubject analyses, indicating enhanced localized phase alignment. The RF model achieves 73.3% accuracy in intrasubject and 67.4% accuracy in intersubject analyses. These findings suggest that propagation features derived from uEMG can effectively characterize pregnancy progression and may aid in monitoring uterine physiological changes throughout gestation.
{"title":"Analysis of Pregnancy Progression in Term Condition Using Propagation Features and Uterine EMG Measurements","authors":"Vinothini S;Punitha N;Karthick P A;Ramakrishnan S","doi":"10.1109/LSENS.2026.3651315","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3651315","url":null,"abstract":"Pregnancy involves various anatomical and physiological changes, with weak and inefficient uterine contractions during early stages. As labor approaches, uterine activity becomes more coordinated due to enhanced synchronization of electrical signals. Monitoring this progression is critical for improving maternal and fetal health through early detection of complications and timely interventions. Uterine electromyography (uEMG) is a noninvasive technique for assessing uterine electrical activity and has emerged as a promising tool for tracking pregnancy progression. This study aims to evaluate synchronization measures derived from multichannel uEMG signals to characterize pregnancy progression. uEMG signals obtained from the publicly available databases are considered during before (T1) and after (T2) 26 weeks of gestation from term delivery. Signals from three bipolar channels, with a fourth derived channel, are preprocessed using a four-pole Butterworth bandpass filter (0.3–3 Hz) to mitigate noise. Propagation features, including maximum cross-correlation, mean coherence, peak coherence, imaginary coherence, and phase locking value (PLV), are extracted across all channel pairs. Random forest (RF)-based feature importance ranking is employed for feature selection, and machine learning classifiers, such as RF, adaptive boosting, and support vector machine, are used for classification. Results show that propagation features are able to characterize the pregnancy progression. Coherence-based measures decrease with increasing gestation, whereas PLV consistently increases in both intersubject and intrasubject analyses, indicating enhanced localized phase alignment. The RF model achieves 73.3% accuracy in intrasubject and 67.4% accuracy in intersubject analyses. These findings suggest that propagation features derived from uEMG can effectively characterize pregnancy progression and may aid in monitoring uterine physiological changes throughout gestation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175648","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}
The rise of electronic waste worldwide over the years has given birth to a new field of research of sustainable, biodegradable, green electronics, which generate minimal waste with less carbon emission. A sustainable platform for continuous sensor monitoring in wearable electronics requires a sustainable, clean, safe, and flexible energy storage solution. Research on paper electronics has seen a major flourishing in recent years, where the need of the hour is to find a sustainable energy storage solution. Recently discovered, MXene electrodes typically use H2SO4-based electrolytes, which are quite toxic and harmful. In this work, as an alternative, a novel Nafion-based gel electrolyte has been developed, operating within the same potential window as H2SO4 (0.6 V). This screen-printed, biocompatible microsupercapacitor (MSC) on paper substrates has an outstanding capacitance of 121 mF cm−2 at a voltage scan rate of 1 mV s−1, with only a single pass of screen printing. This strategy provides stable, inexpensive, environment-friendly, scalable, and flexible on-chip MSCs, paving the way for a next-generation energy storage platform for wearable electronics.
{"title":"Toward Green Electronics: Screen-Printed MXene-Based Microsupercapacitors on Paper Substrate with Nafion-Based Gel Electrolyte","authors":"Sushree Sangita Priyadarsini;Aditi Ghosh;Subho Dasgupta","doi":"10.1109/LSENS.2026.3652104","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3652104","url":null,"abstract":"The rise of electronic waste worldwide over the years has given birth to a new field of research of sustainable, biodegradable, green electronics, which generate minimal waste with less carbon emission. A sustainable platform for continuous sensor monitoring in wearable electronics requires a sustainable, clean, safe, and flexible energy storage solution. Research on paper electronics has seen a major flourishing in recent years, where the need of the hour is to find a sustainable energy storage solution. Recently discovered, MXene electrodes typically use H<sub>2</sub>SO<sub>4</sub>-based electrolytes, which are quite toxic and harmful. In this work, as an alternative, a novel Nafion-based gel electrolyte has been developed, operating within the same potential window as H<sub>2</sub>SO<sub>4</sub> (0.6 V). This screen-printed, biocompatible microsupercapacitor (MSC) on paper substrates has an outstanding capacitance of 121 mF cm<sup>−2</sup> at a voltage scan rate of 1 mV s<sup>−1</sup>, with only a single pass of screen printing. This strategy provides stable, inexpensive, environment-friendly, scalable, and flexible on-chip MSCs, paving the way for a next-generation energy storage platform for wearable electronics.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116899","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 : 2026-01-12DOI: 10.1109/LSENS.2026.3652667
Apinan Aurasopon;J. Jittakort;Sanya Kuankid
This letter presents a simple and accurate interface circuit for three-wire resistive sensors based on a noninverting amplifier and a two-phase ratiometric measurement technique. The circuit is excited by a single positive reference voltage, while an analog switch alternates two current paths to produce distinct steady-state output levels corresponding to different lead-wire configurations. Digital averaging of these steady-state output levels enables effective compensation of lead-wire resistance, op-amp offset, and switch on-resistance effects, with averaging performed digitally after direct ADC sampling. Experimental results demonstrate excellent linearity over the 490–3026 Ω range, corresponding to approximately −130 °C to 525 °C for a Pt1000 sensor, with a maximum relative error of 0.22% and nonlinearity below 0.16% FSS. These results confirm the circuit’s accuracy, simplicity, and suitability for compact, low-power resistive sensor instrumentation.
{"title":"A Simple Noninverting Amplifier for Three-Wire Resistive Sensors Using a Single-Supply Ratiometric Measurement","authors":"Apinan Aurasopon;J. Jittakort;Sanya Kuankid","doi":"10.1109/LSENS.2026.3652667","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3652667","url":null,"abstract":"This letter presents a simple and accurate interface circuit for three-wire resistive sensors based on a noninverting amplifier and a two-phase ratiometric measurement technique. The circuit is excited by a single positive reference voltage, while an analog switch alternates two current paths to produce distinct steady-state output levels corresponding to different lead-wire configurations. Digital averaging of these steady-state output levels enables effective compensation of lead-wire resistance, op-amp offset, and switch <sc>on</small>-resistance effects, with averaging performed digitally after direct ADC sampling. Experimental results demonstrate excellent linearity over the 490–3026 Ω range, corresponding to approximately −130 °C to 525 °C for a Pt1000 sensor, with a maximum relative error of 0.22% and nonlinearity below 0.16% FSS. These results confirm the circuit’s accuracy, simplicity, and suitability for compact, low-power resistive sensor instrumentation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082120","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 : 2026-01-12DOI: 10.1109/LSENS.2026.3651304
Ruijie Liu;Zhiqi Ming;Engang Tian;Hongtian Chen
This letter focuses on the fault detection (FD) problems for a class of nonlinear dynamic systems, particularly in scenarios where conventional methods are prone to failure. Specifically, when a Takagi–Sugeno (T-S) fuzzy model is utilized for system approximation, the residual signal generated exhibits complex nonzero dynamics even under healthy operating conditions, which leads to poor FD performance of traditional residual-based methods. To address this problem, this letter proposes an integrated method that combines a T-S fuzzy soft-sensor with an autoencoder. The method first utilizes the fuzzy soft-sensor to generate residuals, and then the autoencoder is employed to learn the residual patterns under normal operation states. Ultimately, the FD is achieved by monitoring the reconstruction error of the autoencoder, which is quantified as the squared prediction error statistic. The final case study on a ship propulsion system validates the feasibility and superiority of the proposed FD method in detecting both actuator and sensor faults.
{"title":"Integrated Fault Detection Using Fuzzy Soft-Sensor and Autoencoder Techniques for Nonlinear Dynamic Systems","authors":"Ruijie Liu;Zhiqi Ming;Engang Tian;Hongtian Chen","doi":"10.1109/LSENS.2026.3651304","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3651304","url":null,"abstract":"This letter focuses on the fault detection (FD) problems for a class of nonlinear dynamic systems, particularly in scenarios where conventional methods are prone to failure. Specifically, when a Takagi–Sugeno (T-S) fuzzy model is utilized for system approximation, the residual signal generated exhibits complex nonzero dynamics even under healthy operating conditions, which leads to poor FD performance of traditional residual-based methods. To address this problem, this letter proposes an integrated method that combines a T-S fuzzy soft-sensor with an autoencoder. The method first utilizes the fuzzy soft-sensor to generate residuals, and then the autoencoder is employed to learn the residual patterns under normal operation states. Ultimately, the FD is achieved by monitoring the reconstruction error of the autoencoder, which is quantified as the squared prediction error statistic. The final case study on a ship propulsion system validates the feasibility and superiority of the proposed FD method in detecting both actuator and sensor faults.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116916","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 : 2026-01-12DOI: 10.1109/LSENS.2026.3651328
Ahsan Ali;Subha Dharmapalan Puthankattil
Alzheimer’s disease (AD) is a degenerative disorder of the brain that affects elderly individuals, leading to cognitive decline and memory loss. Mild cognitive impairment (MCI) is a transition stage between normal cognition (NC) and AD. Early detection of MCI is crucial since it allows for timely intervention to delay AD progression. The onset of AD is associated with tissue alterations in the fornix, a white matter region of the brain responsible for cognition, learning, memory consolidation, and attention. In this study, fornix morphometrics in MCI and AD are characterized using structural magnetic resonance (sMR) brain images and pseudo-Zernike moment (PZM) features. For this study, a publicly available database is used. Initially, a standard pipeline is used to preprocess the sMR brain images, followed by segmentation of the fornix structure using the level set without reinitialization (LSWR) algorithm. Subsequently, 64 PZMs are computed from the fornix region. Statistical tests, such as the Kolmogorov–Smirnov test, student’s t-test, Wilcoxon–Mann–Whitney test, and one-way analysis of variance are employed to identify significant features, and machine learning algorithms also performed for binary classification. The outcomes revealed that the LSWR algorithm segmented the fornix structure at an accuracy of 99%. The PZM features exhibited statistical significance (p < 0.05) in distinguishing MCI and AD, emphasizing their effectiveness in capturing fornix shape variations. The proposed approach employed in this study emphasizes the clinical relevance in differentiating MCI from NC and AD subjects.
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