Pub Date : 2024-08-07DOI: 10.1109/LSENS.2024.3439720
Krishnadas Narayanan Nampoothiri;Aswathy M Narayanan;Challa Praneeth Kumar
Droplet manipulation using digital microfluidics is extensively being researched for various biological and chemical sensor applications. Among the various methods, liquid dielectrophoresis (L-DEP) offers precise droplet manipulation using high-frequency electric fields. L-DEP also aids in generating temperature inside the droplet. Even though droplet heating using L-DEP promises various potential capabilities in microfluidic sensor development, droplet actuation at high voltages [greater than 400 V peak voltage (V p )] remains a concern. In this manuscript, the parameters, such as dielectric material and dielectric thickness, which are responsible for droplet heating, are investigated numerically through simulations. By keeping the dielectric thickness constant, the relation between temperature rise and frequency for various V p was simulated for seven different dielectrics, mainly Si 3 N 4 , ZnO, and alumina. A temperature rise of 100 °C was generated using V p = 200 V at 200 kHz using Si 3 N 4 as the dielectric, which proves the capability of using this technique at lower voltages. However, the complexity in fabrication hinders its usage in microfluidic applications. Thus, we investigated low dielectric strength materials, such as ZnO and alumina. We observed that despite the dielectric film being porous, due to synthesis, the effect of the porosity of these films in droplet heating is found to be minimal. Finally, the variation of temperature rise inside the droplet with varying dielectric film thickness for various kHz frequencies by keeping the V p is studied. This study is crucial in developing droplet thermal sensors, which could replicate the functions of microheaters for various microfluidic applications.
{"title":"Droplet Heating Using Liquid Dielectrophoresis: A Parametric Study","authors":"Krishnadas Narayanan Nampoothiri;Aswathy M Narayanan;Challa Praneeth Kumar","doi":"10.1109/LSENS.2024.3439720","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3439720","url":null,"abstract":"Droplet manipulation using digital microfluidics is extensively being researched for various biological and chemical sensor applications. Among the various methods, liquid dielectrophoresis (L-DEP) offers precise droplet manipulation using high-frequency electric fields. L-DEP also aids in generating temperature inside the droplet. Even though droplet heating using L-DEP promises various potential capabilities in microfluidic sensor development, droplet actuation at high voltages [greater than 400 V peak voltage (V\u0000<sub>p</sub>\u0000)] remains a concern. In this manuscript, the parameters, such as dielectric material and dielectric thickness, which are responsible for droplet heating, are investigated numerically through simulations. By keeping the dielectric thickness constant, the relation between temperature rise and frequency for various V\u0000<sub>p</sub>\u0000 was simulated for seven different dielectrics, mainly Si\u0000<sub>3</sub>\u0000N\u0000<sub>4</sub>\u0000, ZnO, and alumina. A temperature rise of 100 °C was generated using V\u0000<sub>p</sub>\u0000 = 200 V at 200 kHz using Si\u0000<sub>3</sub>\u0000N\u0000<sub>4</sub>\u0000 as the dielectric, which proves the capability of using this technique at lower voltages. However, the complexity in fabrication hinders its usage in microfluidic applications. Thus, we investigated low dielectric strength materials, such as ZnO and alumina. We observed that despite the dielectric film being porous, due to synthesis, the effect of the porosity of these films in droplet heating is found to be minimal. Finally, the variation of temperature rise inside the droplet with varying dielectric film thickness for various kHz frequencies by keeping the V\u0000<sub>p</sub>\u0000 is studied. This study is crucial in developing droplet thermal sensors, which could replicate the functions of microheaters for various microfluidic applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-07DOI: 10.1109/LSENS.2024.3440044
K. Moatemsu Aier;Jay Chandra Dhar
Zinc oxide nanowires (ZnO NWs) grown using a simple catalytic-free technique called glancing angle deposition retrofitted to a magnetron sputtering unit have been studied for sulfur dioxide (SO 2 ) gas sensing application. The fabricated sensor showed good response (18.19%) toward SO 2 at 300 °C under low ppm concentration (3 ppm) level. Temperature-dependent reaction involved between the ionosorbed surface oxygen and the target gas (SO 2 ) on the large surface area of the ZnO NWs might have played a crucial role in enhancing the sensor response. Furthermore, the as-grown sample showed good selectivity toward different interfering gases, such as NO 2 (2.75%) and CO (1.45%). Also, fast adsorption/desorption kinetics of SO 2 on the NW surface even at low ppm (3 ppm) concentration was observed resulting in good response (41.82 s) and recovery (84.93 s) process of the sensor.
{"title":"SO2 Gas Detection Using GLAD-Synthesized ZnO Nanowires","authors":"K. Moatemsu Aier;Jay Chandra Dhar","doi":"10.1109/LSENS.2024.3440044","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3440044","url":null,"abstract":"Zinc oxide nanowires (ZnO NWs) grown using a simple catalytic-free technique called glancing angle deposition retrofitted to a magnetron sputtering unit have been studied for sulfur dioxide (SO\u0000<sub>2</sub>\u0000) gas sensing application. The fabricated sensor showed good response (18.19%) toward SO\u0000<sub>2</sub>\u0000 at 300 °C under low ppm concentration (3 ppm) level. Temperature-dependent reaction involved between the ionosorbed surface oxygen and the target gas (SO\u0000<sub>2</sub>\u0000) on the large surface area of the ZnO NWs might have played a crucial role in enhancing the sensor response. Furthermore, the as-grown sample showed good selectivity toward different interfering gases, such as NO\u0000<sub>2</sub>\u0000 (2.75%) and CO (1.45%). Also, fast adsorption/desorption kinetics of SO\u0000<sub>2</sub>\u0000 on the NW surface even at low ppm (3 ppm) concentration was observed resulting in good response (41.82 s) and recovery (84.93 s) process of the sensor.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1109/LSENS.2024.3438872
Priya Kaushal;Gargi Khanna
This letter investigates the electrical performance characteristics for breast cancer cell line detection by developing the Si-doped molybdenum disulfide thickness engineered tunnel field effect transistor biosensor. A complete study of the electrostatic field is presented, including the surface potential, electric field, transconductance (g m ), threshold voltage (V th ), on current (I ON ), and subthreshold swing. The sensitivity is analyzed in terms of drain current (I ds ), g m , V th , I ON , I ON /I OFF ratio, and g m . Further, this study investigates the impact of device geometry variations, specifically cavity thickness, and length on the sensitivity of drain current ( $text{S}_{rm{I}_{rm{ds}}}$ ), transconductance ( $text{S}_{rm{g}_{rm{m}}}$ ), threshold voltage ( ${text{S}}_{{{rm{V}}_{{rm{th}}}}}$ ), and on current ( ${text{S}}_{{{rm{I}}_{{rm{ON}}}}}$ ). In addition, the impact of immobilized cell line occupancy on device performance has been examined. The presented biosensor is highly sensitive with increased cavity occupancy resulting in enhanced performance. As a result, array-based screening and diagnosis of breast cancer cells can be accomplished with the device, which is also economical and simpler to fabricate.
{"title":"Breast Cancer Detection Using Si-Doped MoS2 Channel-Based Thickness Engineered TFET Biosensor","authors":"Priya Kaushal;Gargi Khanna","doi":"10.1109/LSENS.2024.3438872","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3438872","url":null,"abstract":"This letter investigates the electrical performance characteristics for breast cancer cell line detection by developing the Si-doped molybdenum disulfide thickness engineered tunnel field effect transistor biosensor. A complete study of the electrostatic field is presented, including the surface potential, electric field, transconductance (g\u0000<sub>m</sub>\u0000), threshold voltage (V\u0000<sub>th</sub>\u0000), \u0000<sc>on</small>\u0000 current (I\u0000<sub>ON</sub>\u0000), and subthreshold swing. The sensitivity is analyzed in terms of drain current (I\u0000<sub>ds</sub>\u0000), g\u0000<sub>m</sub>\u0000, V\u0000<sub>th</sub>\u0000, I\u0000<sub>ON</sub>\u0000, I\u0000<sub>ON</sub>\u0000/I\u0000<sub>OFF</sub>\u0000 ratio, and g\u0000<sub>m</sub>\u0000. Further, this study investigates the impact of device geometry variations, specifically cavity thickness, and length on the sensitivity of drain current (\u0000<inline-formula><tex-math>$text{S}_{rm{I}_{rm{ds}}}$</tex-math></inline-formula>\u0000), transconductance (\u0000<inline-formula><tex-math>$text{S}_{rm{g}_{rm{m}}}$</tex-math></inline-formula>\u0000 ), threshold voltage (\u0000<inline-formula><tex-math>${text{S}}_{{{rm{V}}_{{rm{th}}}}}$</tex-math></inline-formula>\u0000), and \u0000<sc>on</small>\u0000 current (\u0000<inline-formula><tex-math>${text{S}}_{{{rm{I}}_{{rm{ON}}}}}$</tex-math></inline-formula>\u0000). In addition, the impact of immobilized cell line occupancy on device performance has been examined. The presented biosensor is highly sensitive with increased cavity occupancy resulting in enhanced performance. As a result, array-based screening and diagnosis of breast cancer cells can be accomplished with the device, which is also economical and simpler to fabricate.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1109/LSENS.2024.3439259
Jason K. Liao;Chun Yin Lai;Chetwyn Che Hin Chan;Steve W. Y. Mung
This letter introduces an Internet of Things (IoT) acoustic-controlled pillow to reduce environmental noise, monitor snoring, and mitigate insomnia symptoms for enhancing sleep quality. The pillow offers a comprehensive sleep solution by integrating digital hardware active noise control (ANC) technology with an IoT snore tracker. The acoustic control component employs cost-effective digital circuits tailored for effective noise cancellation and music playback functionality, unlike systems using digital signal processors or field-programmable gate arrays. The IoT snore tracker component utilizes mel-frequency cepstral coefficients for precise snore detection, further supported by the capability to upload audio data for further analysis. This dual-functional design not only addresses environmental noise and mitigates insomnia symptoms effectively but also actively monitors snore-related disturbances. Comparative analysis underscores its affordability and practicality, positioning it as a favorable choice for consumers in noisy environments to aid relaxation and sleep induction.
{"title":"Design and Implementation of an IoT Acoustic-Controlled Pillow for Sleep Health Monitoring","authors":"Jason K. Liao;Chun Yin Lai;Chetwyn Che Hin Chan;Steve W. Y. Mung","doi":"10.1109/LSENS.2024.3439259","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3439259","url":null,"abstract":"This letter introduces an Internet of Things (IoT) acoustic-controlled pillow to reduce environmental noise, monitor snoring, and mitigate insomnia symptoms for enhancing sleep quality. The pillow offers a comprehensive sleep solution by integrating digital hardware active noise control (ANC) technology with an IoT snore tracker. The acoustic control component employs cost-effective digital circuits tailored for effective noise cancellation and music playback functionality, unlike systems using digital signal processors or field-programmable gate arrays. The IoT snore tracker component utilizes mel-frequency cepstral coefficients for precise snore detection, further supported by the capability to upload audio data for further analysis. This dual-functional design not only addresses environmental noise and mitigates insomnia symptoms effectively but also actively monitors snore-related disturbances. Comparative analysis underscores its affordability and practicality, positioning it as a favorable choice for consumers in noisy environments to aid relaxation and sleep induction.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1109/LSENS.2024.3438458
Boxuan Xie;Aleksandr D. Kuznetsov;Lauri Mela;Jari Lietzén;Kalle Ruttik;Alp Karakoç;Riku Jäntti
In this letter, we explore an implementation of a novel thin film $1times 4$ reconfigurable intelligent surface (RIS) designed for future communication and sensing scenarios. Utilizing cost-effective inkjet printing methods and additive manufacturing, our approach significantly simplifies the RIS construction process and reduces production costs. The RIS, fabricated on a flexible and lightweight polyethylene terephthalate (PET) substrate, integrates antennas, switching circuitry, and a microcontroller unit (MCU), without a ground shield. This setup enables individual and simultaneous control of each RIS element, manipulating the captured carrier signal by reflecting and refracting its dominant harmonics. Beams of the harmonics can be steered to multiple desired directions at both front and back sides of the surface. Measurement results of the beam steering show that the RIS has the potential to enable RIS-aided communication and sensing applications.
{"title":"Thin Film Reconfigurable Intelligent Surface for Harmonic Beam Steering","authors":"Boxuan Xie;Aleksandr D. Kuznetsov;Lauri Mela;Jari Lietzén;Kalle Ruttik;Alp Karakoç;Riku Jäntti","doi":"10.1109/LSENS.2024.3438458","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3438458","url":null,"abstract":"In this letter, we explore an implementation of a novel thin film \u0000<inline-formula><tex-math>$1times 4$</tex-math></inline-formula>\u0000 reconfigurable intelligent surface (RIS) designed for future communication and sensing scenarios. Utilizing cost-effective inkjet printing methods and additive manufacturing, our approach significantly simplifies the RIS construction process and reduces production costs. The RIS, fabricated on a flexible and lightweight polyethylene terephthalate (PET) substrate, integrates antennas, switching circuitry, and a microcontroller unit (MCU), without a ground shield. This setup enables individual and simultaneous control of each RIS element, manipulating the captured carrier signal by reflecting and refracting its dominant harmonics. Beams of the harmonics can be steered to multiple desired directions at both front and back sides of the surface. Measurement results of the beam steering show that the RIS has the potential to enable RIS-aided communication and sensing applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021689","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 : 2024-08-02DOI: 10.1109/LSENS.2024.3436929
Jie Dou;Fen Hu;Lei Dou
Pedestrian dead reckoning (PDR) utilizes foot-mounted inertial sensors as a State-of-the-Art technique for indoor positioning. In this letter, we introduce an integration of a factor graph optimization (FGO) framework with navigation data from dual-foot-mounted inertial measurement units (IMUs), thus enhancing the accuracy of pedestrian localization. The use of FGO allows for the effective utilization of historical sensor data to improve current state estimation accuracy. Recognizing the potential for sensor error drift over time, we have developed a factor node tailored with pedestrian stride constraints to mitigate error propagation. We conducted several experiments with two low-cost IMUs to evaluate the effectiveness of our proposed method. Supported by numerical analysis, the results show that by incorporating historical information, FGO better explores the correlation between the two feet to significantly improve positioning accuracy, although it increases the computational time, which is negligible.
{"title":"Factor Graph Optimization Enhanced Pedestrian Dead Reckoning With Dual-Foot-Mounted IMUs","authors":"Jie Dou;Fen Hu;Lei Dou","doi":"10.1109/LSENS.2024.3436929","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3436929","url":null,"abstract":"Pedestrian dead reckoning (PDR) utilizes foot-mounted inertial sensors as a State-of-the-Art technique for indoor positioning. In this letter, we introduce an integration of a factor graph optimization (FGO) framework with navigation data from dual-foot-mounted inertial measurement units (IMUs), thus enhancing the accuracy of pedestrian localization. The use of FGO allows for the effective utilization of historical sensor data to improve current state estimation accuracy. Recognizing the potential for sensor error drift over time, we have developed a factor node tailored with pedestrian stride constraints to mitigate error propagation. We conducted several experiments with two low-cost IMUs to evaluate the effectiveness of our proposed method. Supported by numerical analysis, the results show that by incorporating historical information, FGO better explores the correlation between the two feet to significantly improve positioning accuracy, although it increases the computational time, which is negligible.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973465","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}
Diabetes presents a significant global health challenge, necessitating precise blood glucose monitoring for effective management. Continuous glucose monitoring (CGM) devices offer a minimally invasive approach, yet require accurate calibration models to improve reliability. This letter investigates various neural network architectures for predicting time errors in CGM sensor readings, aiming for high accuracy and minimal computational burden. Using simulated data, models were trained and evaluated, with Legendre memory unit and temporal convolutional network architectures emerging as promising candidates. With these architectures, it was possible to lower the sensor's reading error to, respectively, 24.22 and 25.34 mg/dL, decreasing the error by 40.6% and 37.9%. Furthermore, the letter explores the impact of sensor calibration frequency on prediction accuracy, revealing optimal performance with calibrations once every three or five days, obtaining an error in the reading of approximately 16 and 15 mg/dL. These findings underscore the potential for enhancing glucose monitoring systems and suggest avenues for future research in diabetes management.
{"title":"Optimizing Glucose Sensor Calibration With Lightweight Neural Networks: A Comparative Study","authors":"Costanza Cenerini;Anna Sabatini;Luca Vollero;Danilo Pau","doi":"10.1109/LSENS.2024.3436630","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3436630","url":null,"abstract":"Diabetes presents a significant global health challenge, necessitating precise blood glucose monitoring for effective management. Continuous glucose monitoring (CGM) devices offer a minimally invasive approach, yet require accurate calibration models to improve reliability. This letter investigates various neural network architectures for predicting time errors in CGM sensor readings, aiming for high accuracy and minimal computational burden. Using simulated data, models were trained and evaluated, with Legendre memory unit and temporal convolutional network architectures emerging as promising candidates. With these architectures, it was possible to lower the sensor's reading error to, respectively, 24.22 and 25.34 mg/dL, decreasing the error by 40.6% and 37.9%. Furthermore, the letter explores the impact of sensor calibration frequency on prediction accuracy, revealing optimal performance with calibrations once every three or five days, obtaining an error in the reading of approximately 16 and 15 mg/dL. These findings underscore the potential for enhancing glucose monitoring systems and suggest avenues for future research in diabetes management.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1109/LSENS.2024.3436106
Kanishk Singh;Chao-Hung Chen;Li-Chia Tai;Wei-Chen Huang;Tung-Ming Pan
In this research work, we present a flexible iridium oxide (IrO x ) extended-gate field-effect transistor (EGFET) biosensor for label-free detection of the epidermal growth factor receptor (EGFR) biomarker. IrO x was employed as the sensing membrane material due to its excellent electrochemical properties, high conductivity, stability, biocompatibility, and ability to facilitate redox reactions. The IrO x film was deposited on a flexible polyimide (PI) substrate through a sol–gel process and characterized using atomic force microscopy and X-ray photoelectron spectroscopy. As a pH sensor, the IrO x EGFET exhibited a super-Nernstian sensitivity of 68.54 mV/pH with high linearity, a low hysteresis of ∼ 6 mV, and a drift rate of 0.50 mV/h at pH 7 over 12 h. For EGFR detection, the IrO x surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde to immobilize anti-EGFR antibodies. The EGFR sensor demonstrated a wide linear detection range from 1 to 1000 ng/mL with a sensitivity of 11.78 mV/log(ng/mL) and a linearity of 99.8%, making it suitable for clinical EGFR monitoring in cancer patients. The flexible IrO x EGFET shows promise for reliable label-free detection of EGFR.
{"title":"Label-Free EGFR Sensing by Using a Flexible IrOx Extended-Gate Field-Effect Transistor-Based Biosensor","authors":"Kanishk Singh;Chao-Hung Chen;Li-Chia Tai;Wei-Chen Huang;Tung-Ming Pan","doi":"10.1109/LSENS.2024.3436106","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3436106","url":null,"abstract":"In this research work, we present a flexible iridium oxide (IrO\u0000<italic><sub>x</sub></i>\u0000) extended-gate field-effect transistor (EGFET) biosensor for label-free detection of the epidermal growth factor receptor (EGFR) biomarker. IrO\u0000<italic><sub>x</sub></i>\u0000 was employed as the sensing membrane material due to its excellent electrochemical properties, high conductivity, stability, biocompatibility, and ability to facilitate redox reactions. The IrO\u0000<italic><sub>x</sub></i>\u0000 film was deposited on a flexible polyimide (PI) substrate through a sol–gel process and characterized using atomic force microscopy and X-ray photoelectron spectroscopy. As a pH sensor, the IrO\u0000<italic><sub>x</sub></i>\u0000 EGFET exhibited a super-Nernstian sensitivity of 68.54 mV/pH with high linearity, a low hysteresis of ∼ 6 mV, and a drift rate of 0.50 mV/h at pH 7 over 12 h. For EGFR detection, the IrO\u0000<italic><sub>x</sub></i>\u0000 surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde to immobilize anti-EGFR antibodies. The EGFR sensor demonstrated a wide linear detection range from 1 to 1000 ng/mL with a sensitivity of 11.78 mV/log(ng/mL) and a linearity of 99.8%, making it suitable for clinical EGFR monitoring in cancer patients. The flexible IrO\u0000<italic><sub>x</sub></i>\u0000 EGFET shows promise for reliable label-free detection of EGFR.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973519","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}
Multisensor fusion simultaneous localization and mapping (SLAM) has gained popularity in the SLAM community due to its low cost and high real-time performance. Common point-feature-based visual-inertial SLAM systems often struggle in environments with weak textures or motion blur. By incorporating line features, the accuracy and robustness of SLAM systems can be improved. However, challenges in line matching and increased processing time caused by line features have limited these improvements. To address the problem, we introduce a real-time monocular visual-inertial SLAM method with inertial measurement unit (IMU)-aided hybrid line matching, where the hybrid lines consist of elementary and recessive lines. Specifically, an IMU-aided hybrid line matching scheme is designed to determine the search space of line matching according to the IMU preintegration result. It scales down the search range effectively and thus improves the accuracy and speed of line matching. Also, an improved enhanced line segment drawing (iELSED) algorithm is utilized for efficient elementary line feature extraction, where the parameters of line features are adaptively adjusted with the number of extracted point features to avoid feature redundancy. In addition, we also extend the point-based loop-closure detection by introducing line features for higher accuracy of loop-closure detection. Experiment results demonstrate the effectiveness of the proposed method.
多传感器融合同步定位与制图（SLAM）因其低成本和高实时性在 SLAM 领域大受欢迎。普通的基于点特征的视觉惯性 SLAM 系统往往在纹理较弱或运动模糊的环境中难以发挥作用。通过结合线特征，可以提高 SLAM 系统的准确性和鲁棒性。然而，线形特征带来的线形匹配挑战和处理时间的增加限制了这些改进。为了解决这个问题，我们引入了一种实时单目视觉惯性 SLAM 方法，该方法采用惯性测量单元（IMU）辅助混合线匹配，其中混合线由基本线和隐性线组成。具体来说，我们设计了一种惯性测量单元辅助混合线匹配方案，根据惯性测量单元的预积分结果确定线匹配的搜索空间。它能有效缩小搜索范围，从而提高线段匹配的精度和速度。同时，利用改进的增强线段绘制算法（iELSED）进行高效的基本线段特征提取，线段特征参数随提取点特征的数量进行自适应调整，以避免特征冗余。此外，我们还通过引入线特征来扩展基于点的闭环检测，从而提高闭环检测的准确性。实验结果证明了所提方法的有效性。
{"title":"Monocular Visual-Inertial SLAM With IMU-Aided Hybrid Line Matching","authors":"Gongpu Zha;Peiyu Guan;Zhiqiang Cao;Ting Sun;Shijie Yu","doi":"10.1109/LSENS.2024.3435988","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3435988","url":null,"abstract":"Multisensor fusion simultaneous localization and mapping (SLAM) has gained popularity in the SLAM community due to its low cost and high real-time performance. Common point-feature-based visual-inertial SLAM systems often struggle in environments with weak textures or motion blur. By incorporating line features, the accuracy and robustness of SLAM systems can be improved. However, challenges in line matching and increased processing time caused by line features have limited these improvements. To address the problem, we introduce a real-time monocular visual-inertial SLAM method with inertial measurement unit (IMU)-aided hybrid line matching, where the hybrid lines consist of elementary and recessive lines. Specifically, an IMU-aided hybrid line matching scheme is designed to determine the search space of line matching according to the IMU preintegration result. It scales down the search range effectively and thus improves the accuracy and speed of line matching. Also, an improved enhanced line segment drawing (iELSED) algorithm is utilized for efficient elementary line feature extraction, where the parameters of line features are adaptively adjusted with the number of extracted point features to avoid feature redundancy. In addition, we also extend the point-based loop-closure detection by introducing line features for higher accuracy of loop-closure detection. Experiment results demonstrate the effectiveness of the proposed method.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/LSENS.2024.3435719
Nurul Amin Choudhury;Sakshi Singh;Badal Soni
Gait recognition is a biometric technology that identifies individuals based on their unique way of walking. Most of the work on human gait recognition (HGR) systems has minimal user records and is performed in a closed simulated environment, which hampers the performance in a real-world scenario. This letter presents an efficient ensemble framework using a hybrid deep learning network (convolutional neural network-long short-term memory) with an extra tree classifier (ETC) for HGR in a real-world environment. The proposed model effectively extracts low-level spatial and temporal features from the sensor data for meaningful pattern generation and classifies them using multiple decision trees present in the ensemble ETC. A State-of-the-Art HGR dataset has also been developed for a diverse set of users in uncontrolled environments in real-world environments using built-in smartphone sensors. The proposed model achieved an average performance accuracy of 99.10% and optimal precision, recall, and F1-score, outperforming all the benchmark models with optimal performance margins in lower computational times.
步态识别是一种生物识别技术，可根据个人独特的行走方式对其进行识别。大多数关于人类步态识别（HGR）系统的研究都只有极少的用户记录，而且都是在封闭的模拟环境中进行的，这就影响了其在真实世界场景中的表现。这封信提出了一种高效的集合框架，它使用混合深度学习网络（卷积神经网络-长短期记忆）和额外树分类器（ETC），用于真实世界环境中的 HGR。所提出的模型能有效地从传感器数据中提取低层次的空间和时间特征，从而生成有意义的模式，并利用集合 ETC 中的多个决策树对其进行分类。此外，还利用内置智能手机传感器开发了一个最新的 HGR 数据集，该数据集针对现实世界环境中不同用户在不受控制的环境中的情况。所提出的模型实现了 99.10% 的平均准确率，以及最佳的精确度、召回率和 F1 分数，以较低的计算时间和最佳的性能余量超越了所有基准模型。
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