This letter presents a comprehensive study of the source of nonlinearities in a novel z-axis microelectromechanical systems (MEMS) accelerometer fabricated using a two-silicon-layer fabrication process. The device features a unique mechanical architecture that converts the out-of-plane motion of the proof mass into linear in-plane displacement of the sensing frames, enabling efficient capacitive readout. Initial experimental characterization revealed an unexpected nonlinearity, exceeding predictions of the ideal mechanical model. To investigate the origin of this behavior, a detailed 3-D finite element method (FEM) analysis was performed, incorporating fabrication-induced effects such as substrate deformation and residual stresses. Simulations demonstrated that substrate deformation has negligible impact within the operational range, while residual prestresses on the structural silicon layer strongly influence the device response, producing nonlinearity levels consistent with experimental measurements. The close agreement between FEM predictions and experimental data validates the model and identifies residual prestresses on the structural silicon layer as the dominant factor affecting the device linearity. These insights provide a clear pathway for future design optimization, suggesting that careful control of residual stress and potential structural modifications can significantly improve the performance, linearity, and reliability of subsequent generations of z-axis MEMS accelerometers.
{"title":"Understanding the Nonlinear Behavior of a New z-Axis MEMS Accelerometer With In-Plane Readout","authors":"Yassine Banani;Christian Padovani;Giacomo Langfelder;Gabriele Gattere;Valentina Zega","doi":"10.1109/LSENS.2025.3638964","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3638964","url":null,"abstract":"This letter presents a comprehensive study of the source of nonlinearities in a novel <italic>z</i>-axis microelectromechanical systems (MEMS) accelerometer fabricated using a two-silicon-layer fabrication process. The device features a unique mechanical architecture that converts the out-of-plane motion of the proof mass into linear in-plane displacement of the sensing frames, enabling efficient capacitive readout. Initial experimental characterization revealed an unexpected nonlinearity, exceeding predictions of the ideal mechanical model. To investigate the origin of this behavior, a detailed 3-D finite element method (FEM) analysis was performed, incorporating fabrication-induced effects such as substrate deformation and residual stresses. Simulations demonstrated that substrate deformation has negligible impact within the operational range, while residual prestresses on the structural silicon layer strongly influence the device response, producing nonlinearity levels consistent with experimental measurements. The close agreement between FEM predictions and experimental data validates the model and identifies residual prestresses on the structural silicon layer as the dominant factor affecting the device linearity. These insights provide a clear pathway for future design optimization, suggesting that careful control of residual stress and potential structural modifications can significantly improve the performance, linearity, and reliability of subsequent generations of <italic>z</i>-axis MEMS accelerometers.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778318","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}
Radar-based respiratory measurement is a promising tool for the noncontact detection of sleep apnea. Our team has reported that apnea events can be accurately detected using the statistical characteristics of the amplitude of respiratory displacement. However, apnea and hypopnea events are often followed by irregular breathing, reducing the detection accuracy. This study proposes a new method to overcome this performance degradation by repeatedly applying the detection method to radar data sets corresponding to multiple overlapping time intervals. Averaging the detected classes over multiple time intervals gives an analog value between 0 and 1, which can be interpreted as the probability of apnea and hypopnea events occurring. We show that the proposed method can mitigate the effect of irregular breathing that occurs after apnea and hypopnea events. The performance was validated using overnight recordings from seven patients, showing a 1.4-fold improvement in apnea and hypopnea event detection compared with the nonoverlapping method.
{"title":"Accurate Radar-Based Detection of Sleep Apnea Using Overlapping Time-Interval Averaging","authors":"Kodai Hasegawa;Shigeaki Okumura;Hirofumi Taki;Hironobu Sunadome;Satoshi Hamada;Susumu Sato;Kazuo Chin;Takuya Sakamoto","doi":"10.1109/LSENS.2025.3639141","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3639141","url":null,"abstract":"Radar-based respiratory measurement is a promising tool for the noncontact detection of sleep apnea. Our team has reported that apnea events can be accurately detected using the statistical characteristics of the amplitude of respiratory displacement. However, apnea and hypopnea events are often followed by irregular breathing, reducing the detection accuracy. This study proposes a new method to overcome this performance degradation by repeatedly applying the detection method to radar data sets corresponding to multiple overlapping time intervals. Averaging the detected classes over multiple time intervals gives an analog value between 0 and 1, which can be interpreted as the probability of apnea and hypopnea events occurring. We show that the proposed method can mitigate the effect of irregular breathing that occurs after apnea and hypopnea events. The performance was validated using overnight recordings from seven patients, showing a 1.4-fold improvement in apnea and hypopnea event detection compared with the nonoverlapping method.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886660","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}
Gas pipeline leaks in industrial environment can be hazardous, necessitating timely remediation. Owing to the shortcomings of contact-based sensing methods, noncontact localization and diagnostics is necessary. Leak severity, alongside leak localization, is a critical parameter requiring long-term monitoring due to its nonstationary nature. Existing algorithms become computationally expensive in such conditions. Thus, a lightweight data-driven approach is necessary. It has been experimentally found that during training phase, the leak severity gets coupled with leak position due to the directional nature of a leak with respect to the sensor. This work proposes a leak-position-informed neural network training method for leak severity classification. The proposed approach has been compared with various training and testing methods to evaluate their ability to predict leak severity. The proposed method yields accuracy of 93% and 83% in clean and noisy environment over a range of experimental conditions.
{"title":"A Data-Driven Approach to Leak Identification and Severity Analysis in Pipelines Using Acoustic Sensing and Deep Learning","authors":"Mayukh Biswas;Aditya Narayan;Debaudh Ghosh;Samriddha Ganguly;Raj Rakshit;Chirabrata Bhaumik","doi":"10.1109/LSENS.2025.3638602","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3638602","url":null,"abstract":"Gas pipeline leaks in industrial environment can be hazardous, necessitating timely remediation. Owing to the shortcomings of contact-based sensing methods, noncontact localization and diagnostics is necessary. Leak severity, alongside leak localization, is a critical parameter requiring long-term monitoring due to its nonstationary nature. Existing algorithms become computationally expensive in such conditions. Thus, a lightweight data-driven approach is necessary. It has been experimentally found that during training phase, the leak severity gets coupled with leak position due to the directional nature of a leak with respect to the sensor. This work proposes a leak-position-informed neural network training method for leak severity classification. The proposed approach has been compared with various training and testing methods to evaluate their ability to predict leak severity. The proposed method yields accuracy of 93% and 83% in clean and noisy environment over a range of experimental conditions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778187","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 : 2025-11-28DOI: 10.1109/LSENS.2025.3638591
O. Le Traon;J. Bonhomme;L. Hudeley;M. Duquesnoy;C. Duclos;A. Andrieux-Ledier;P. Lavenus;J. Guerard;R. Levy
This letter presents the concept of a quartz crystal Coriolis vibrating gyroscope named GYTRIX (GYro for TRIgonal piezoelectric Xtal) able to operate in matched modes using force to rebalance or in the whole angle mode. The symmetry of the design that respects quartz crystal trigonal symmetry enables two degenerated modes with nominally identical thermomechanical behavior. This new gyro concept is presented, along with the first step in its development roadmap: the design of a separated-mode open-loop gyro transducer, in order to validate the concept with existing open-loop electronics. Prototype realization and experimental measurements made it possible to validate theoretical angular random walk of 0.017°/√h and to demonstrate gyrocompass operation.
这封信介绍了一个名为GYTRIX （GYro for TRIgonal压电Xtal）的石英晶体科里奥利振动陀螺仪的概念，该陀螺仪能够在匹配模式下使用力来重新平衡或在整个角度模式下运行。设计的对称性尊重石英晶体的三角对称，使两种简并模式具有名义上相同的热力学行为。提出了这种新的陀螺仪概念，以及其发展路线图的第一步：设计一种分离模式开环陀螺仪换能器，以便与现有的开环电子设备验证该概念。样机的实现和实验测量使得理论角度随机游走0.017°/√h和陀螺罗经操作的验证成为可能。
{"title":"GYTRIX Quartz MEMS Gyro: From Concept to Northfinding Measurements","authors":"O. Le Traon;J. Bonhomme;L. Hudeley;M. Duquesnoy;C. Duclos;A. Andrieux-Ledier;P. Lavenus;J. Guerard;R. Levy","doi":"10.1109/LSENS.2025.3638591","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3638591","url":null,"abstract":"This letter presents the concept of a quartz crystal Coriolis vibrating gyroscope named GYTRIX (GYro for TRIgonal piezoelectric Xtal) able to operate in matched modes using force to rebalance or in the whole angle mode. The symmetry of the design that respects quartz crystal trigonal symmetry enables two degenerated modes with nominally identical thermomechanical behavior. This new gyro concept is presented, along with the first step in its development roadmap: the design of a separated-mode open-loop gyro transducer, in order to validate the concept with existing open-loop electronics. Prototype realization and experimental measurements made it possible to validate theoretical angular random walk of 0.017°/√h and to demonstrate gyrocompass operation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830974","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 work explores the interactions of hemoglobin (Hb) with noble metal nanoparticles (NPs) to probe its redox activity. Screen-printed electrodes (SPEs) modified with Au, Pt, and Pd NPs exhibit significant improvement in the electrochemical signals of Hb. Differential pulse voltammetry measurements show maximum Fe3+ reduction currents of Hb using AuNP/SPE. Such response is attributed to NP-induced conformational changes in Hb. Fourier transform infrared spectroscopy reveals that NPs promote protein unfolding, which leads to exposure of the iron-containing heme group from the hydrophobic pockets of Hb. Surface tension and contact angle analysis of Hb further support such observation. AuNPs induce maximum conformational alterations in Hb, consequently facilitating effective electron transport.
{"title":"Investigating the Impact of Noble Metal Nanoparticle Decorated Electrodes on Electrochemical Sensing of Hemoglobin","authors":"Aindrila Roy;Baishali Basak;Rajdeep Ganguly;Subhadip Chakraborty;Ananya Barui;Rajen Haldar;Sanatan Chattopadhyay","doi":"10.1109/LSENS.2025.3638008","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3638008","url":null,"abstract":"The work explores the interactions of hemoglobin (Hb) with noble metal nanoparticles (NPs) to probe its redox activity. Screen-printed electrodes (SPEs) modified with Au, Pt, and Pd NPs exhibit significant improvement in the electrochemical signals of Hb. Differential pulse voltammetry measurements show maximum Fe<sup>3+</sup> reduction currents of Hb using AuNP/SPE. Such response is attributed to NP-induced conformational changes in Hb. Fourier transform infrared spectroscopy reveals that NPs promote protein unfolding, which leads to exposure of the iron-containing heme group from the hydrophobic pockets of Hb. Surface tension and contact angle analysis of Hb further support such observation. AuNPs induce maximum conformational alterations in Hb, consequently facilitating effective electron transport.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778369","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 : 2025-11-27DOI: 10.1109/LSENS.2025.3637753
Xin-Min Pan;Siyuan Yang;Kah Chan Teh;Sirajudeen Gulam Razul;Alex Chichung Kot
The loss of information from occlusions increases the complexity of remote sensing, with vision-based methods failing in situations of full occlusion. Radio frequency (RF) methods in the WiFi spectrum rely on signals that can pass through some obstacles, such as walls, thereby overcoming known limitations of vision-based perception, such as poor lighting and obstruction of the subject. As such, this letter illustrates a proof-of-concept of through-wall human pose estimation (HPE) using WiFi-like signals, proposing a dual-channel transmitter–receiver setup behind two orthogonal walls. Using a matched filter, four sets of data are collected simultaneously, corresponding to two sets of monostatic and bistatic Doppler data. Leveraging the benefits of skip connections in convolutional neural networks, the experiments employ existing deep learning architectures, achieving an average error in the same order of magnitude as state-of-the-art RF HPE methods. The usage of all four sets of data yields a 62.01-mm average error across all joints and actions, and an average error of 73.13 mm for a single-channel setup with just a single set of monostatic Doppler data.
{"title":"Through-Wall Human Pose Estimation With WiFi","authors":"Xin-Min Pan;Siyuan Yang;Kah Chan Teh;Sirajudeen Gulam Razul;Alex Chichung Kot","doi":"10.1109/LSENS.2025.3637753","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3637753","url":null,"abstract":"The loss of information from occlusions increases the complexity of remote sensing, with vision-based methods failing in situations of full occlusion. Radio frequency (RF) methods in the WiFi spectrum rely on signals that can pass through some obstacles, such as walls, thereby overcoming known limitations of vision-based perception, such as poor lighting and obstruction of the subject. As such, this letter illustrates a proof-of-concept of through-wall human pose estimation (HPE) using WiFi-like signals, proposing a dual-channel transmitter–receiver setup behind two orthogonal walls. Using a matched filter, four sets of data are collected simultaneously, corresponding to two sets of monostatic and bistatic Doppler data. Leveraging the benefits of skip connections in convolutional neural networks, the experiments employ existing deep learning architectures, achieving an average error in the same order of magnitude as state-of-the-art RF HPE methods. The usage of all four sets of data yields a 62.01-mm average error across all joints and actions, and an average error of 73.13 mm for a single-channel setup with just a single set of monostatic Doppler data.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729295","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 : 2025-11-27DOI: 10.1109/LSENS.2025.3637817
Jin Mitsugi;Osamu Tokumasu;Masashi Owaki
This letter presents a novel battery-free wireless strain sensing system consisting of an interrogator and single or multiple streaming sensors. A streaming sensor comprises a radio frequency integrated circuit (RFIC), a commercial off-the-shelf strain gauge, and an analog-to-digital converter (ADC). Backscatter communication in the 920 MHz band is employed to both power the sensor and to establish communication. The emerging ISO/IEC 18000-65 (FDIS), which supports concurrent backscatter streaming, is adopted as the communication protocol. The ADC sampling rate and the backscatter frequency channel are over-the-air configurable through a glue logic between the RFIC and the ADC. The proposed system was evaluated in an indoor propagation environment. It was experimentally demonstrated that the proposed system can achieve an error free concurrent strain measurement with a measurement resolution in the order of $10^{-5}$ and reading distance of 1.8 m using a 4-W effective isotropic radiation power software defined interrogator.
{"title":"Concurrent Battery-Free Wireless Strain Sensing Using Backscatter Communication","authors":"Jin Mitsugi;Osamu Tokumasu;Masashi Owaki","doi":"10.1109/LSENS.2025.3637817","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3637817","url":null,"abstract":"This letter presents a novel battery-free wireless strain sensing system consisting of an interrogator and single or multiple streaming sensors. A streaming sensor comprises a radio frequency integrated circuit (RFIC), a commercial off-the-shelf strain gauge, and an analog-to-digital converter (ADC). Backscatter communication in the 920 MHz band is employed to both power the sensor and to establish communication. The emerging ISO/IEC 18000-65 (FDIS), which supports concurrent backscatter streaming, is adopted as the communication protocol. The ADC sampling rate and the backscatter frequency channel are over-the-air configurable through a glue logic between the RFIC and the ADC. The proposed system was evaluated in an indoor propagation environment. It was experimentally demonstrated that the proposed system can achieve an error free concurrent strain measurement with a measurement resolution in the order of <inline-formula><tex-math>$10^{-5}$</tex-math></inline-formula> and reading distance of 1.8 m using a 4-W effective isotropic radiation power software defined interrogator.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674801","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 designs and implements a passive wireless resonant frequency testing system of sensors based on point frequency measurement technology, aiming to address the issues of high hardware costs and excessive power consumption in traditional sweep-frequency techniques for wireless sensor networks. The system achieved rapid resonance frequency calibration through fixed-frequency excitation, optimized impedance matching circuit design, and efficient coordination of RF transceiver modules. In order to estimate the performance of the passive wireless test system, it is used as an LC-resonant sensor for displacement and pressure detection. Displacement tests revealed a linear frequency tuning characteristic of −2.33 MHz/mm; pressure tests exhibited nonlinear sensitivity, showing a sensitivity of −1.25 MHz/N in the low-pressure region (0–20 N) and −0.04 MHz/N in the high-pressure region (20–100 N). Test results confirmed reduced power consumption, a significantly improved signal-to-noise ratio, a frequency measurement standard deviation of 1.47 MHz, and a maximum frequency offset of 3.36 MHz compared to traditional sweep-frequency approaches. This letter provides a feasible technical solution for low-cost, high-precision monitoring in wireless sensor networks.
{"title":"Design and Application of Passive Wireless Testing System","authors":"Junzhe Shen;Huiyang Yu;Junjie Zhang;Chentao Wang;Jianqiu Huang","doi":"10.1109/LSENS.2025.3637757","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3637757","url":null,"abstract":"This letter designs and implements a passive wireless resonant frequency testing system of sensors based on point frequency measurement technology, aiming to address the issues of high hardware costs and excessive power consumption in traditional sweep-frequency techniques for wireless sensor networks. The system achieved rapid resonance frequency calibration through fixed-frequency excitation, optimized impedance matching circuit design, and efficient coordination of RF transceiver modules. In order to estimate the performance of the passive wireless test system, it is used as an <italic>LC</i>-resonant sensor for displacement and pressure detection. Displacement tests revealed a linear frequency tuning characteristic of −2.33 MHz/mm; pressure tests exhibited nonlinear sensitivity, showing a sensitivity of −1.25 MHz/N in the low-pressure region (0–20 N) and −0.04 MHz/N in the high-pressure region (20–100 N). Test results confirmed reduced power consumption, a significantly improved signal-to-noise ratio, a frequency measurement standard deviation of 1.47 MHz, and a maximum frequency offset of 3.36 MHz compared to traditional sweep-frequency approaches. This letter provides a feasible technical solution for low-cost, high-precision monitoring in wireless sensor networks.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830976","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 : 2025-11-25DOI: 10.1109/LSENS.2025.3636894
Sukanya Mahalik;Bidesh Mahata;Sayan Dey
In this work, we demonstrated an MoS$_{2}$/Graphene oxide (GO) inverted back-gated thin-film transistor (TFT) for selective detection of NO$_{x}$ in dual-operational regimes. Few-layered MoS$_{2}$ was synthesized by liquid exfoliation technique while GO was synthesized by the modified Hummer’s method. The as-fabricated device was exposed to calculated concentrations of NO and NO$_{2}$ gases varying from 1 to 30 ppm, and its sensing performance was analyzed. The device demonstrated a distinct region-dependent selectivity under ambient conditions (300 K), detecting NO in the saturation region at V$_{GS}$ of −1 V and a V$_{DS}$ of −4.5 V, achieving a maximum response of 58% at 30 ppm of NO. In contrast, a highly selective response to NO$_{2}$ under similar gate bias conditions was observed in the linear region, with a V$_{DS}$ of −2 V, a response of 40% at 30 ppm of NO$_{2}$. The limit of detection for NO and NO$_{2}$ at the abovementioned optimized conditions was found to be 27.5 and 195 ppb, respectively. Hence, the proposed device, with its region-based sensing capabilities, offers a cost-effective and low-power alternative to traditional gas sensor arrays by reducing the effective device dimensions and associated design complexities.
{"title":"An MoS$_{2}$/GO-Based Thin-Film Transistor for Region-Dependent Tunable Detection of NO$_{x}$ Gases","authors":"Sukanya Mahalik;Bidesh Mahata;Sayan Dey","doi":"10.1109/LSENS.2025.3636894","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3636894","url":null,"abstract":"In this work, we demonstrated an MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>/Graphene oxide (GO) inverted back-gated thin-film transistor (TFT) for selective detection of NO<inline-formula><tex-math>$_{x}$</tex-math></inline-formula> in dual-operational regimes. Few-layered MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> was synthesized by liquid exfoliation technique while GO was synthesized by the modified Hummer’s method. The as-fabricated device was exposed to calculated concentrations of NO and NO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> gases varying from 1 to 30 ppm, and its sensing performance was analyzed. The device demonstrated a distinct region-dependent selectivity under ambient conditions (300 K), detecting NO in the saturation region at V<inline-formula><tex-math>$_{GS}$</tex-math></inline-formula> of −1 V and a V<inline-formula><tex-math>$_{DS}$</tex-math></inline-formula> of −4.5 V, achieving a maximum response of 58% at 30 ppm of NO. In contrast, a highly selective response to NO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> under similar gate bias conditions was observed in the linear region, with a V<inline-formula><tex-math>$_{DS}$</tex-math></inline-formula> of −2 V, a response of 40% at 30 ppm of NO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>. The limit of detection for NO and NO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> at the abovementioned optimized conditions was found to be 27.5 and 195 ppb, respectively. Hence, the proposed device, with its region-based sensing capabilities, offers a cost-effective and low-power alternative to traditional gas sensor arrays by reducing the effective device dimensions and associated design complexities.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729332","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 : 2025-11-24DOI: 10.1109/LSENS.2025.3636670
Thurimerla Prasanth;Ram Prasad Padhy;B Sivaselvan
Sensors play a fundamental role in sensing the environment for autonomous vehicle perception, providing accurate and reliable data essential for understanding and navigating the surroundings. LiDAR sensors are widely used for their ability to generate detailed 3-D point cloud data of the surroundings. Bird’s-Eye View (BEV) detection utilizes these point cloud data to identify objects, such as cars and cyclists, from a top–down perspective. This LiDAR sensor-based perception approach is essential for understanding complex environments and ensuring safe navigation in real-time driving scenarios. This letter presents DSFNet, a compact LiDAR-only network for BEV perception. The model integrates an efficient pillar-based encoder with a proposed dual-scale fusion (DSF) backbone, designed to mitigate performance and complexity issues associated with LiDAR sensors. The backbone reduces parameter count by approximately 50% compared to standard architectures while maintaining competitive detection accuracy. By capturing both local detail and global context, DSFNet enhances feature representation for sparse and irregular LiDAR data. Evaluations on the official KITTI BEV benchmark demonstrate strong performance in car and cyclist detection, highlighting suitability for real-time sensor-driven applications.
{"title":"LiDAR Sensor-Based Dual-Scale Fusion Approach for Bird’s-Eye View Sensing in Autonomous Vehicles","authors":"Thurimerla Prasanth;Ram Prasad Padhy;B Sivaselvan","doi":"10.1109/LSENS.2025.3636670","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3636670","url":null,"abstract":"Sensors play a fundamental role in sensing the environment for autonomous vehicle perception, providing accurate and reliable data essential for understanding and navigating the surroundings. LiDAR sensors are widely used for their ability to generate detailed 3-D point cloud data of the surroundings. Bird’s-Eye View (BEV) detection utilizes these point cloud data to identify objects, such as cars and cyclists, from a top–down perspective. This LiDAR sensor-based perception approach is essential for understanding complex environments and ensuring safe navigation in real-time driving scenarios. This letter presents DSFNet, a compact LiDAR-only network for BEV perception. The model integrates an efficient pillar-based encoder with a proposed dual-scale fusion (DSF) backbone, designed to mitigate performance and complexity issues associated with LiDAR sensors. The backbone reduces parameter count by approximately 50% compared to standard architectures while maintaining competitive detection accuracy. By capturing both local detail and global context, DSFNet enhances feature representation for sparse and irregular LiDAR data. Evaluations on the official KITTI BEV benchmark demonstrate strong performance in car and cyclist detection, highlighting suitability for real-time sensor-driven applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705920","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}
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