Pub Date : 2024-11-21DOI: 10.1109/LSENS.2024.3504336
Klara Hänisch;Sarah J. Spitzner;Niloofar Saeedzadeh Khaanghah;Rakesh R. Nair;Tobias Antrack;Hans Kleemann;Karl Leo
Biodegradable electronics open a path to new sensing applications with minimum ecological impact. In particular, biodegradable pressure sensors may enable new concepts in medical monitoring, human–machine interfaces, or tracking of industrial processes. A crucial step toward biodegradable electronics is to identify suitable and ideally bio-sourced base materials for the fabrication of substrates, conductors, and other functional layers. Here, we present a fully biodegradable pressure sensor with natural leaf skeletons as the main functional component. Leaves are used as the source material for the fabrication of the electrodes and the dielectric layers of our capacitive pressure sensors. The fabricated sensors yield sensitivities similar to state-of-the-art devices in a pressure range of about 1–50 kPa. Hence, these fully bio-degradable systems may enable applications in various areas, such as medicine, agriculture, and industrial processing.
{"title":"Flexible Pressure Sensors Based on Biodegradable Leaf Scaffolds","authors":"Klara Hänisch;Sarah J. Spitzner;Niloofar Saeedzadeh Khaanghah;Rakesh R. Nair;Tobias Antrack;Hans Kleemann;Karl Leo","doi":"10.1109/LSENS.2024.3504336","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3504336","url":null,"abstract":"Biodegradable electronics open a path to new sensing applications with minimum ecological impact. In particular, biodegradable pressure sensors may enable new concepts in medical monitoring, human–machine interfaces, or tracking of industrial processes. A crucial step toward biodegradable electronics is to identify suitable and ideally bio-sourced base materials for the fabrication of substrates, conductors, and other functional layers. Here, we present a fully biodegradable pressure sensor with natural leaf skeletons as the main functional component. Leaves are used as the source material for the fabrication of the electrodes and the dielectric layers of our capacitive pressure sensors. The fabricated sensors yield sensitivities similar to state-of-the-art devices in a pressure range of about 1–50 kPa. Hence, these fully bio-degradable systems may enable applications in various areas, such as medicine, agriculture, and industrial processing.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10762873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810621","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 : 2024-11-21DOI: 10.1109/LSENS.2024.3504333
Aditta Chowdhury;Mehdi Hasan Chowdhury;M. Ali Akber Dewan;Ray C.C. Cheung
Hemoglobin is an integral part of blood, and its abnormality indicates various diseases. Different noninvasive methods are developed to predict the concentration of hemoglobin. Previous studies verified the potential of photoplethysmogram (PPG) signals in estimating the health parameter. Although different hardware tools have been used to develop digital systems over the years, they lack the reconfigurability feature needed to develop a point-of-care (POC) system. In this study, a field programmable gate array (FPGA)-based reconfigurable hardware system, including preprocessor, memory and control, feature extractor and classifier subsystems, is designed targeting Zynq 7000 Zedboard. The system utilizes six features extracted from the PPG signals collected using DCM08 PPG sensor and linear regression classifier model for prediction. PPG signals based on four different wavelengths of light are tested, and the best result has been achieved with infrared light having a wavelength of 940 nm, which will help to design PPG sensors for wearable and medical devices. The mean absolute error with this wavelength is 2.55 g/L with an error rate of 1.78%. The power consumption analysis validates the designed system to be a low-power device. The designed processor can be used as a POC system, and due to its reconfigurable advantage, the system can be further improved by adding other health parameter predictions and disease detection.
{"title":"Reconfigurable Point-of-Care System for Hemoglobin Estimation From Photoplethysmogram","authors":"Aditta Chowdhury;Mehdi Hasan Chowdhury;M. Ali Akber Dewan;Ray C.C. Cheung","doi":"10.1109/LSENS.2024.3504333","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3504333","url":null,"abstract":"Hemoglobin is an integral part of blood, and its abnormality indicates various diseases. Different noninvasive methods are developed to predict the concentration of hemoglobin. Previous studies verified the potential of photoplethysmogram (PPG) signals in estimating the health parameter. Although different hardware tools have been used to develop digital systems over the years, they lack the reconfigurability feature needed to develop a point-of-care (POC) system. In this study, a field programmable gate array (FPGA)-based reconfigurable hardware system, including preprocessor, memory and control, feature extractor and classifier subsystems, is designed targeting Zynq 7000 Zedboard. The system utilizes six features extracted from the PPG signals collected using DCM08 PPG sensor and linear regression classifier model for prediction. PPG signals based on four different wavelengths of light are tested, and the best result has been achieved with infrared light having a wavelength of 940 nm, which will help to design PPG sensors for wearable and medical devices. The mean absolute error with this wavelength is 2.55 g/L with an error rate of 1.78%. The power consumption analysis validates the designed system to be a low-power device. The designed processor can be used as a POC system, and due to its reconfigurable advantage, the system can be further improved by adding other health parameter predictions and disease detection.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844589","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 : 2024-11-20DOI: 10.1109/LSENS.2024.3502813
T. Sunil Kumar;Daniel Ranta;Daniel Rönnow;Patrik Ottosson
Determining moisture content (MC) in wood chips finds its application in many industries, including energy production. In this letter, we aim to develop an automated method for determining MC in woodchips using ultrawideband (UWB) radio signals and machine learning algorithms. First, to acquire UWB signals through wood chips on conveyor belts in industrial plants, we use measurement devices with a radio transmitter and receiver, and a laser sensor to determine the thickness of the wood chips. UWB and laser data corresponding to 1923 samples from four power plants is acquired. Second, we extract the amplitude and delay-based features, and these are finally fed to three different machine learning algorithms, namely, linear regression, artificial neural network (ANN), and ensemble trees to determine the MC. The proposed method achieves best results when the ANN is used. More specifically, our method achieves a mean absolute error (MAE) of 2.75% when the features from both UWB and laser sensors are used for determining MC. The MAE of 3.95% is achieved when features only from UWB data (without the laser) are used for determining MC. Our results for industrial data suggest that the proposed method is effective for determining MC in industrial applications.
{"title":"Determining the Moisture Content of Wood Chips in Inline Industry Applications Using UWB Radio Transmission Signals and Machine Learning","authors":"T. Sunil Kumar;Daniel Ranta;Daniel Rönnow;Patrik Ottosson","doi":"10.1109/LSENS.2024.3502813","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3502813","url":null,"abstract":"Determining moisture content (MC) in wood chips finds its application in many industries, including energy production. In this letter, we aim to develop an automated method for determining MC in woodchips using ultrawideband (UWB) radio signals and machine learning algorithms. First, to acquire UWB signals through wood chips on conveyor belts in industrial plants, we use measurement devices with a radio transmitter and receiver, and a laser sensor to determine the thickness of the wood chips. UWB and laser data corresponding to 1923 samples from four power plants is acquired. Second, we extract the amplitude and delay-based features, and these are finally fed to three different machine learning algorithms, namely, linear regression, artificial neural network (ANN), and ensemble trees to determine the MC. The proposed method achieves best results when the ANN is used. More specifically, our method achieves a mean absolute error (MAE) of 2.75% when the features from both UWB and laser sensors are used for determining MC. The MAE of 3.95% is achieved when features only from UWB data (without the laser) are used for determining MC. Our results for industrial data suggest that the proposed method is effective for determining MC in industrial applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789029","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 : 2024-11-19DOI: 10.1109/LSENS.2024.3501586
Agastasya Dahiya;Dhruv Wadhwa;Rohan Katti;Luigi G. Occhipinti
Gesture recognition is an important element of human–computer interaction that allows natural and intuitive communication in applications such as healthcare, rehabilitation, smart home environments, safety, gaming, and accessibility solutions for individuals with disabilities. The electromyography (EMG) and mechanomyography (MMG) sensor-based traditional approaches suffer from limitations such as noise susceptibility, critical placement requirements, and inefficient detection of broader arm movements. Further, they do not work for individuals with amputation or minimal muscle movement, as muscle activity is not available. Addressing these challenges, herein, we present a novel wearable hand gesture recognition system which is less prone to noise and placement issues. The presented devices use accelerometers and gyroscopes to capture hand and arm gestures. Further, the developed wearable system employs 1-D convolutional neural networks (1-D CNNs), long short-term memory, and recurrent neural networks for efficient processing of data and recognition of gestures. The 1-D CNN with three convolutional and three dense layers emerged as the optimal solution, achieving an accuracy of 97.88% with balanced inference time and memory usage. The study concludes that this model offers an optimal trade-off between model size and accuracy, making it highly suitable for resource-constrained wearable devices.
{"title":"Efficient Hand Gesture Recognition Using Artificial Intelligence and IMU-Based Wearable Device","authors":"Agastasya Dahiya;Dhruv Wadhwa;Rohan Katti;Luigi G. Occhipinti","doi":"10.1109/LSENS.2024.3501586","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3501586","url":null,"abstract":"Gesture recognition is an important element of human–computer interaction that allows natural and intuitive communication in applications such as healthcare, rehabilitation, smart home environments, safety, gaming, and accessibility solutions for individuals with disabilities. The electromyography (EMG) and mechanomyography (MMG) sensor-based traditional approaches suffer from limitations such as noise susceptibility, critical placement requirements, and inefficient detection of broader arm movements. Further, they do not work for individuals with amputation or minimal muscle movement, as muscle activity is not available. Addressing these challenges, herein, we present a novel wearable hand gesture recognition system which is less prone to noise and placement issues. The presented devices use accelerometers and gyroscopes to capture hand and arm gestures. Further, the developed wearable system employs 1-D convolutional neural networks (1-D CNNs), long short-term memory, and recurrent neural networks for efficient processing of data and recognition of gestures. The 1-D CNN with three convolutional and three dense layers emerged as the optimal solution, achieving an accuracy of 97.88% with balanced inference time and memory usage. The study concludes that this model offers an optimal trade-off between model size and accuracy, making it highly suitable for resource-constrained wearable devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761433","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 : 2024-11-19DOI: 10.1109/LSENS.2024.3502156
Artem T. Tulaev;V. V. Loboda
This letter presents a method for parametric extraction of sensing elements of silicon-on-insulator (SOI) microelectromechanical system pressure sensors utilizing SPICE simulation with integrated circuit (IC) electronic interface. This approach allows the performance optimization of sensing elements and readout electronics in early design stage. The piezoresistive sensing element based on SOI technology is manufactured by means of deep reactive ion etching on predoped SOI wafers. The finite-element model (FEM) of the sensing element is used for SPICE sensor model extraction. These parameters translate to a Verilog-A sensing element model. The set of simulation with IC electronic interface consists of an instrumentation amplifier was performed. The 11% difference in fullscale (FS) nonlinearity for the FEM and the SPICE model was obtained.
{"title":"Parametric Extraction Method of Equivalent Circuit for SOI MEMS Pressure Sensor Rapid SPICE Simulation","authors":"Artem T. Tulaev;V. V. Loboda","doi":"10.1109/LSENS.2024.3502156","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3502156","url":null,"abstract":"This letter presents a method for parametric extraction of sensing elements of silicon-on-insulator (SOI) microelectromechanical system pressure sensors utilizing SPICE simulation with integrated circuit (IC) electronic interface. This approach allows the performance optimization of sensing elements and readout electronics in early design stage. The piezoresistive sensing element based on SOI technology is manufactured by means of deep reactive ion etching on predoped SOI wafers. The finite-element model (FEM) of the sensing element is used for SPICE sensor model extraction. These parameters translate to a Verilog-A sensing element model. The set of simulation with IC electronic interface consists of an instrumentation amplifier was performed. The 11% difference in fullscale (FS) nonlinearity for the FEM and the SPICE model was obtained.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810559","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 : 2024-11-18DOI: 10.1109/LSENS.2024.3500135
Hafiz Ahmed;Bojan Mavkov
This letter addresses the system identification and control of a throttle valve (TV) from a production engine perspective. Despite advances in control theory and AI, industrial controllers still often use conventional proportional–integral (PI) techniques for the TV. However, the TV's inherent system and sensor nonlinearities challenge the PI controller's ability to maintain satisfactory tracking across diverse operating conditions. This letter upgrades the conventional PI controller with a nonlinear error function and introduces a single-stage indirect closed-loop system identification using simulated annealing optimization. Detailed procedures for the identification process and controller development are provided. A comparative performance analysis shows that the nonlinear modification can reduce the root-mean-square tracking error by up to 70%, making the nonlinear PI (NPI) controller a strong alternative to traditional counterparts.
{"title":"Magnetic Angle Sensor-Assisted Identification and Control of a Throttle Valve","authors":"Hafiz Ahmed;Bojan Mavkov","doi":"10.1109/LSENS.2024.3500135","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3500135","url":null,"abstract":"This letter addresses the system identification and control of a throttle valve (TV) from a production engine perspective. Despite advances in control theory and AI, industrial controllers still often use conventional proportional–integral (PI) techniques for the TV. However, the TV's inherent system and sensor nonlinearities challenge the PI controller's ability to maintain satisfactory tracking across diverse operating conditions. This letter upgrades the conventional PI controller with a nonlinear error function and introduces a single-stage indirect closed-loop system identification using simulated annealing optimization. Detailed procedures for the identification process and controller development are provided. A comparative performance analysis shows that the nonlinear modification can reduce the root-mean-square tracking error by up to 70%, making the nonlinear PI (NPI) controller a strong alternative to traditional counterparts.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777891","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 : 2024-11-18DOI: 10.1109/LSENS.2024.3501772
Khaled Bin Easin;Mohiminur Rahman Ifty;Md. Sadik Al Rayhan;Saptami Rani;Nazmun Nahar Maria;Md. Arafat Hossain;Protik Chandra Biswas
A self-contained smartphone-based near infrared (NIR) colorimeter is reported for the first time by utilizing the inbuilt NIR emitter ( $Delta lambda approx 770 !-! 1000$ nm) and macrolens complementary metal-oxide semiconductor (CMOS) camera of the phone. The emitted NIR light is guided through a low-cost multimode plastic fiber to illuminate the test sample, and the phone's CMOS camera is utilized to collect and digitize the transmitted light through the sample. An easy-to-use customized Android app has been developed to operate this ultra-low-cost (<$2)>${{lambda }_p} approx 930$ nm, which complies with the operating wavelength band ( $Delta lambda approx 910 !-! 980$ nm) of this instrument.
{"title":"Smartphone Self-Contained NIR Colorimeter","authors":"Khaled Bin Easin;Mohiminur Rahman Ifty;Md. Sadik Al Rayhan;Saptami Rani;Nazmun Nahar Maria;Md. Arafat Hossain;Protik Chandra Biswas","doi":"10.1109/LSENS.2024.3501772","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3501772","url":null,"abstract":"A self-contained smartphone-based near infrared (NIR) colorimeter is reported for the first time by utilizing the inbuilt NIR emitter (\u0000<inline-formula><tex-math>$Delta lambda approx 770 !-! 1000$</tex-math></inline-formula>\u0000nm) and macrolens complementary metal-oxide semiconductor (CMOS) camera of the phone. The emitted NIR light is guided through a low-cost multimode plastic fiber to illuminate the test sample, and the phone's CMOS camera is utilized to collect and digitize the transmitted light through the sample. An easy-to-use customized Android app has been developed to operate this ultra-low-cost (<$2)><tex-math>${{lambda }_p} approx 930$</tex-math></inline-formula>\u0000 nm, which complies with the operating wavelength band (\u0000<inline-formula><tex-math>$Delta lambda approx 910 !-! 980$</tex-math></inline-formula>\u0000nm) of this instrument.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777912","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 : 2024-11-18DOI: 10.1109/LSENS.2024.3500785
Manh-Hung Vu;Thi-Thao Tran;Van-Truong Pham;Men-Tzung Lo
Diagnosing bearing faults is an important issue in the field of electrical machines, where approximately 40 $%$ of faults in electrical machines are caused by bearings. With the development of deep learning, diagnosing bearing faults from vibration signals helps reduce costs and time while increasing diagnostic accuracy. However, traditional deep learning models need to be trained from large and diverse datasets to be able to provide good diagnostic results, which is not suitable for specific data such as bearings because it can be difficult to collect data and require expensive resources. In this letter, a new diagnostic method is proposed based on few-shot learning to overcome the data problem. The proposed method synthesizes information from both spatial-level and channel-level to find information in the condition of only little training data, improving diagnostic accuracy. Besides, selective aggregation feature extraction is proposed to replace the traditional convolution neural network to extract condensed features that carry more information. For instance, with only 30 training samples, the model achieves 86.67% accuracy on the CWRU dataset, this suggested method obtains State-of-the-Art results, demonstrating its efficacy.
{"title":"Multilevel Few-Shot Model With Selective Aggregation Feature for Bearing Fault Diagnosis Under Limited Data Condition","authors":"Manh-Hung Vu;Thi-Thao Tran;Van-Truong Pham;Men-Tzung Lo","doi":"10.1109/LSENS.2024.3500785","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3500785","url":null,"abstract":"Diagnosing bearing faults is an important issue in the field of electrical machines, where approximately 40\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of faults in electrical machines are caused by bearings. With the development of deep learning, diagnosing bearing faults from vibration signals helps reduce costs and time while increasing diagnostic accuracy. However, traditional deep learning models need to be trained from large and diverse datasets to be able to provide good diagnostic results, which is not suitable for specific data such as bearings because it can be difficult to collect data and require expensive resources. In this letter, a new diagnostic method is proposed based on few-shot learning to overcome the data problem. The proposed method synthesizes information from both spatial-level and channel-level to find information in the condition of only little training data, improving diagnostic accuracy. Besides, selective aggregation feature extraction is proposed to replace the traditional convolution neural network to extract condensed features that carry more information. For instance, with only 30 training samples, the model achieves 86.67% accuracy on the CWRU dataset, this suggested method obtains State-of-the-Art results, demonstrating its efficacy.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844481","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}
Traumatic brain injury (TBI) is physical damage to the brain and a significant cause of mortality and morbidity affecting all ages worldwide, remaining as a diagnostic and therapeutic challenge to date. The design and development of rapid, low cost, highly accurate, and long-term stable point-of-care TBI diagnostic test kits is an unmet clinical need. In light of this, here we report a novel multianalyte chemicapacitive immunosensing platform that can detect FDA-approved Glial Fibrillary Acidic Protein (GFAP) biomarkers in real-time human plasma samples using carboxylic functionalized MWCNTs (fMWCNTs) embedded Polypyrrole (PPY) as a bioelectrical transducer. Herein, the low-cost GFAP bioelectrodes were prepared through covalent immobilization of anti-GFAP-antibodies on PPY-fMWCNTs modified array of interdigitated microelectrodes (IDµEs, fabricated on low-cost single-side copper clad PCB substrates). The binding event of GFAP peptides with anti-GFAP-antibodies in real-time human plasma samples was captured in terms of ac capacitance measured through C-F analysis (using an Agilent B1500A parametric analyzer) and quantified in terms of normalized change in capacitance of GFAP bioelectrodes with and without exposure of target GFAP peptides spiked in real-time human plasma samples (10 fg/mL – 1 µg/mL). The proposed PPY-fMWCNTs nanocomposite-based chemicapacitive immunosensing platform effectively detected GFAP target analytes in linear detection range 10 fg/mL – 10 ng/mL with a sensitivity and LoD of 3.9743 ((ΔC/C 0 )/ng·mL −1 )/cm 2 and 0.3854 fg/mL, respectively. Further, it also showed superior performance in terms of selectivity, reproducibility, long-term stability (30 weeks) and interference resistance. The proposed ac-capacitive approach is facile, label-free and can be combined with dc-resistive measurements to improve the diversity of decision-making parameters that inherently aid in improving the diagnostic accuracy of TBI test kit.
{"title":"PPY-fMWCNT Nanocomposite-Based Chemicapacitive Biosensor for Ultrasensitive Detection of TBI-Specific GFAP Biomarker in Human Plasma","authors":"Patta Supraja;Rahul Gangwar;Suryasnata Tripathy;Siva Rama Krishna Vanjari;Shiv Govind Singh","doi":"10.1109/LSENS.2024.3497003","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3497003","url":null,"abstract":"Traumatic brain injury (TBI) is physical damage to the brain and a significant cause of mortality and morbidity affecting all ages worldwide, remaining as a diagnostic and therapeutic challenge to date. The design and development of rapid, low cost, highly accurate, and long-term stable point-of-care TBI diagnostic test kits is an unmet clinical need. In light of this, here we report a novel multianalyte chemicapacitive immunosensing platform that can detect FDA-approved Glial Fibrillary Acidic Protein (GFAP) biomarkers in real-time human plasma samples using carboxylic functionalized MWCNTs (fMWCNTs) embedded Polypyrrole (PPY) as a bioelectrical transducer. Herein, the low-cost GFAP bioelectrodes were prepared through covalent immobilization of anti-GFAP-antibodies on PPY-fMWCNTs modified array of interdigitated microelectrodes (IDµEs, fabricated on low-cost single-side copper clad PCB substrates). The binding event of GFAP peptides with anti-GFAP-antibodies in real-time human plasma samples was captured in terms of ac capacitance measured through C-F analysis (using an Agilent B1500A parametric analyzer) and quantified in terms of normalized change in capacitance of GFAP bioelectrodes with and without exposure of target GFAP peptides spiked in real-time human plasma samples (10 fg/mL – 1 µg/mL). The proposed PPY-fMWCNTs nanocomposite-based chemicapacitive immunosensing platform effectively detected GFAP target analytes in linear detection range 10 fg/mL – 10 ng/mL with a sensitivity and LoD of 3.9743 ((ΔC/C\u0000<sub>0</sub>\u0000)/ng·mL\u0000<sup>−1</sup>\u0000)/cm\u0000<sup>2</sup>\u0000 and 0.3854 fg/mL, respectively. Further, it also showed superior performance in terms of selectivity, reproducibility, long-term stability (30 weeks) and interference resistance. The proposed ac-capacitive approach is facile, label-free and can be combined with dc-resistive measurements to improve the diversity of decision-making parameters that inherently aid in improving the diagnostic accuracy of TBI test kit.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713907","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}
With the increasing demand for high temporal and spatial resolution multispectral data sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 sensor data. However, current fusion methods suffer from the loss of detailed spatial and spectral information. To address this problem, a Landsat-8 and Sentinel-2 data fusion with multiscale detailed information (MSDI) method is proposed. MSDI combines well the initial spatial prediction obtained from the Landsat-8 data at the target date and the detailed part extracted from the Sentinel-2 data at the reference date. Thin plate spline interpolation is implemented on the Landsat-8 data for upsampling. Smoothing-sharpening filter (SSIF) is employed to separate the high- and low-frequency components of data from the two sensors. The multiscale SSIF is then utilized to migrate the details from the Sentinel-2 data to the upsampled Landsat-8 data. Experiments at two sites confirm that the proposed MSDI method could efficiently generate Sentinel-2-like data with high spatial and spectral resolution.
{"title":"Landsat-8 Sensor and Sentinel-2 Sensor Data Fusion With Multiscale Detailed Information","authors":"Peng Wang;Jun Du;Xiongfei Wen;Caiping Hu;Lin Ge;Mingxuan Huang","doi":"10.1109/LSENS.2024.3499361","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3499361","url":null,"abstract":"<?Firstputimage>\u0000With the increasing demand for high temporal and spatial resolution multispectral data sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 sensor data. However, current fusion methods suffer from the loss of detailed spatial and spectral information. To address this problem, a Landsat-8 and Sentinel-2 data fusion with multiscale detailed information (MSDI) method is proposed. MSDI combines well the initial spatial prediction obtained from the Landsat-8 data at the target date and the detailed part extracted from the Sentinel-2 data at the reference date. Thin plate spline interpolation is implemented on the Landsat-8 data for upsampling. Smoothing-sharpening filter (SSIF) is employed to separate the high- and low-frequency components of data from the two sensors. The multiscale SSIF is then utilized to migrate the details from the Sentinel-2 data to the upsampled Landsat-8 data. Experiments at two sites confirm that the proposed MSDI method could efficiently generate Sentinel-2-like data with high spatial and spectral resolution.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757855","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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