{"title":"Significance of CLPSO-Based Dataset in Self-Supervised Lightweight ANN for Estimating Highly Intelligible Microphone Sensor Location","authors":"Ritujoy Biswas;Diksha Bhat;Karan Nathwani","doi":"10.1109/LSENS.2025.3534471","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3534471","url":null,"abstract":"This letter proposes training a lightweight artificial neural network (ANN) in a self-supervised manner using an optimal dataset generated via comprehensive learning particle swarm optimization (CLPSO). Although CLPSO can suggest the “optimal” microphone sensor locations in a room relative to a speaker, it is computationally taxing. Instead, we propose using these suggested sensor locations as implicit labels for training a network. It is suggested to use five-best sensor locations for training instead of one to ensure that the model captures the relationship between the speaker and the sensor locations within the room. This training is done on a resource-constrained Raspberry Pi. The trained ANN quickly predicts good sensor locations corresponding to high intelligibility in terms of short-time objective intelligibility (STOI). This performance is generalized across different combinations of room dimensions and speaker locations and is robust for varying datasets. The predictions were also validated in real-world conditions through mean opinion score (MOS) values.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379542","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}
Alim Gulbag;Michael Huang;Brian Rong;Benjamin Sreenan;Xiaoshan Zhu
{"title":"A Simple Circuit for Time-Resolved Luminescence (TRL) Measurement Instruments: Demonstration Through a Smartphone-Based TRL Imager for Anticounterfeiting Application","authors":"Alim Gulbag;Michael Huang;Brian Rong;Benjamin Sreenan;Xiaoshan Zhu","doi":"10.1109/LSENS.2025.3535901","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3535901","url":null,"abstract":"Time-resolved luminescence (TRL) measurement is a sensitive detection technique by eliminating sample autofluorescence, but TRL measurement instruments composed of multiple key components (e.g., a rapidly pulsed light excitation source, a time-gated optical detector, and a synchronization module aligning the timing between the light source and the detector) have been sophisticated, expensive, or bulky, which limits their point-of-care or in-field applications. To reduce the cost and complexity of these instruments, in this letter, we developed a simple circuit for rapid LED pulsing and accurate timing synchronization and implemented it in a compact TRL imager with a ultraviolet light emitting diode (UV LED) as a light source and a chopper-coupled smartphone camera as a time-gated optical detector. The TRL measurement of this imager using this circuit was successfully validated through an anticounterfeiting application. We believe that this simple circuit can be adopted in the development of low-cost and compact TRL measurement instruments for broad point-of-care or in-field applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446329","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}
Reena Dey;Thomas T. Daniel;Emlin Elsa Abraham;Vimal Kumar Singh Yadav;Roy Paily
{"title":"Room Temperature Printed Ethanol Sensor Based on P-Phenylenediamine Functionalized Graphene Oxide","authors":"Reena Dey;Thomas T. Daniel;Emlin Elsa Abraham;Vimal Kumar Singh Yadav;Roy Paily","doi":"10.1109/LSENS.2025.3535770","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3535770","url":null,"abstract":"In this letter, an ethanol sensor operating at room temperature is fabricated utilizing graphene oxide functionalized with p-phenylenediamine as the sensing channel and printed silver nanoparticle film as the electrodes over a glass substrate. The electrodes are developed using a rapid and cost-effective microcantilever-based printing technology. The fabricated sensor is exposed to varying concentrations of volatile organic compounds, such as acetone, isopropanol, and ethanol, to analyze its response and selectivity. The sensor shows high selectivity and a sensitivity of 33% toward five parts per million ethanol.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430576","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}
{"title":"Development of Microwave Imaging System Using UWB Antenna Array for Inhomogeneity Detection in Human Brain Phantoms","authors":"Yogesh Kumar Yadav;Kushmanda Saurav;Sahil Kalra","doi":"10.1109/LSENS.2025.3535756","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3535756","url":null,"abstract":"This study introduces a method for diagnosing multiple brain tumors using an array of eight compact ultrawideband (UWB) antennas. Employing radar technology, UWB monopole antennas encircle a brain phantom to detect inhomogeneities. Designed and characterized via a commercial electromagnetic solver, the antennas operate within a 1–6 GHz frequency range. A 3-D brain and tumor phantoms are fabricated, and eight antennas array based microwave imaging system is implemented. The system reconstructed radar-based images by analyzing back-scattered signals, using the delay-and-sum algorithm for tumor detection. Results show the system effectively identifies single or multiple brain tumors, with high correlation between measured and simulated data.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388556","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}
{"title":"Quartz Crystal Microbalance-Tag Apparatus for Wireless Mass Sensing Applications","authors":"Danidu Dilmith Jayathilaka Sinhalathilakage;Howgen Kesuma Pratama;Narayanan Ramakrishnan","doi":"10.1109/LSENS.2025.3534996","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3534996","url":null,"abstract":"We report a near-field communication (NFC)-enabled quartz crystal microbalance (QCM)-tag instrumentation suitable for wireless mass loading sensing applications. The proposed instrumentation utilizes a low-power microcontroller-based Pierce oscillator frequency counter system to measure series resonance frequency <inline-formula><tex-math>$(f_{0})$</tex-math></inline-formula> of the QCM over a time period. The system was able to record the change in <inline-formula><tex-math>$f_{0},(Delta f)$</tex-math></inline-formula> caused by mass loading to the NFC's electrically erasable programmable read-only memory. The <inline-formula><tex-math>$f_{0}$</tex-math></inline-formula> measured using the proposed system was then validated against impedance analyzer measurement, and the maximum error was measured to be 0.0004%. An Android application was then developed to read log <inline-formula><tex-math>$f_{0}$</tex-math></inline-formula> using a smartphone. Further, we demonstrated wireless measurement of the mass loading rate of a dc sputtering system, a well-known vacuum deposition technique adopted in semiconductor process industries. Accordingly, the proposed system is an inexpensive plug-and-play solution for real-time wireless in-situ measurement of mass loading changes in sealed chambers or hazardous environments where manual measurement or wired measurement is difficult.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388564","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}
{"title":"Blind Extraction-Based Multichannel Speech Enhancement in Noisy and Reverberation Environments","authors":"Yuan Xie;Tao Zou;Weijun Sun;Shengli Xie","doi":"10.1109/LSENS.2025.3533642","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3533642","url":null,"abstract":"Speech enhancement has important applications in sensor, hearing aids, robotics, and video conferencing. However, the speech enhancement performance is severely deteriorated by additional background noise and high reverberations. To solve the problem of speech enhancement in noisy and acoustically reverberant scenarios, this letter proposes a multichannel speech enhancement algorithm based on blind extraction to achieve speech denoising and dereverberation. First, a new model for speech enhancement is constructed by assuming the reverberations generated by later reflections as additional and unrelated noise components. Subsequently, a blind signal extraction approach is designed to extract the direct sound and early reflected sounds, achieving dereverberation and denoising. Experimental results confirm that the proposed algorithm achieves better speech enhancement in noisy and acoustic reverberation scenarios and that the effect of dereverberation and noise reduction is superior to that of popular speech enhancement algorithms, especially in high reverberation environments.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521455","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}
Bhavesh Raj Singh Nehra;Devika S Kumar;Anoop Chandrika Sreekantan
{"title":"An Efficient Linearizing Demodulator Interface for LVDT","authors":"Bhavesh Raj Singh Nehra;Devika S Kumar;Anoop Chandrika Sreekantan","doi":"10.1109/LSENS.2025.3533036","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3533036","url":null,"abstract":"In this letter, we introduce an efficient linearizing demodulator circuit for linear variable differential transformers (LVDTs). A key innovation of this circuit lies in the integration of linearization and demodulation functionalities within simple electronics, while effectively addressing the limitations of traditional LVDT interfaces. It uses a simple saw-tooth excitation for LVDT and incorporates an enhanced inverse synthesis circuit as the core signal conditioner for processing the LVDT's secondary outputs. Further, this approach offers other unique features, including first, compensation for transients, rise-time, and other common LVDT errors, second, low component count, third, non-requirement of precision oscillator and demodulators, and fourth, a broad range. The efficacy of the proposed method was evaluated through simulation and experimental studies, showing a remarkable improvement in linearity (over six times) across a 100 mm span. The developed system achieved a nonlinearity error of 0.9%, a signal-to-noise ratio of 52 dB, and a repeatability error of 0.014%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379557","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}
R. K. Mandal;S. Dalai;Chandan Jana;R. Barua;Subhajit Maur;B. Chatterjee;Susanta Ray;Sovan Dalai
{"title":"Condition Sensing of Overhead Line Insulator Strings Using an Advanced CNN-Based Classifier Based on Infrared Thermography","authors":"R. K. Mandal;S. Dalai;Chandan Jana;R. Barua;Subhajit Maur;B. Chatterjee;Susanta Ray;Sovan Dalai","doi":"10.1109/LSENS.2025.3532290","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3532290","url":null,"abstract":"This letter proposes an advanced convolutional neural network (CNN)-based classifier for detecting the contamination level of in-service insulator strings. The goal is to enhance condition monitoring of insulators and ensure safe and reliable power system operation under adverse weather conditions and polluted environments. All possible partial and full contamination cases of a string of three disc insulators have been considered. Infrared thermography images taken from a safe distance have been cropped to consider the effective portions before being fed into a convolution-based deep neural network. The classifier has been trained with a total of 1248 thermal images across 12 contamination classes achieving an accuracy level of 99.04%. The proposed classifier has also been compared with two other benchmark CNN models.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455096","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}
{"title":"RespTrack-Net: Respiration Parameters Tracking From PPG Signal Using Deep Learning Model","authors":"Amit Bhongade;Prathosh AP;Tapan Kumar Gandhi","doi":"10.1109/LSENS.2025.3532445","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3532445","url":null,"abstract":"Photoplethysmography (PPG) signals are widely used for nonintrusive health monitoring, but existing methods often struggle with noise susceptibility and computational complexity, limiting their practical utility. This research introduces two key innovations: the wearable low-cost PPG acquisition device (WeLOVE) and the RespTrack-Net model. The WeLOVE device is designed to provide high-quality PPG signal acquisition at low cost, addressing the accessibility challenges of current systems. The RespTrack-Net model introduces a novel architecture tailored for extracting respiration rate (RR) and cardiovascular parameters with enhanced robustness to noise and motion artifacts. The proposed approach was validated using two datasets: an experimental database (eight subjects) collected in this study and the publicly available CapnoBase database (42 subjects). RespTrack-Net achieved mean absolute errors of 1.58 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.30 and 3.16 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 3.36 for RR estimation on these datasets, respectively, outperforming State-of-the-Art methods. These contributions demonstrate the system's novelty and potential for reliable, real-time health monitoring in diverse settings. Future research will explore the use of the proposed device for sleep apnea detection, offering a cost-effective and comfortable alternative to current polysomnography (PSG) methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361075","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}
Thijs Van Hauwermeiren;Anatolii Sianov;Annelies Coene;Guillaume Crevecoeur
{"title":"Tweelie: Tactile Wheel-Shaped Sensor for Force Reconstruction and Localization Over Curved Spherical Surface","authors":"Thijs Van Hauwermeiren;Anatolii Sianov;Annelies Coene;Guillaume Crevecoeur","doi":"10.1109/LSENS.2025.3531939","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3531939","url":null,"abstract":"This letter introduces Tweelie: a tactile wheel-shaped sensor with soft elastomer skin based on barometric pressure transducers. Tweelie enables high impact force reconstruction and localization over a curved spherical surface. Multiple contacts occurring simultaneously can be detected and inferred over a 6457 <inline-formula><tex-math>$text{mm}^{2}$</tex-math></inline-formula> surface. Based on the spatial distribution of 48 micro-electromechanical system (MEMS) sensors along a cylindrical surface, a graph is constructed to infer the contact state. The 3-D force localization is done by mapping the pressure readings onto an appropriate pressure distribution based on the shape of the Tweelie sensor; the 3-D force is obtained by integrating this distribution. Results show a localization error of 2<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula> and regression error of less than 1 N for a single contact on rigid surface, enabling direct force localization and reconstruction for locomotion and other tactile applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379558","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}