Pub Date : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639816
Abdullah S. Alharthi, K. Ozanyan
Human gait pattens remain largely undefined when relying on a single sensing modality. We report a pilot implementation of sensor fusion to classify gait spatiotemporal signals, from a publicly available dataset of 50 participants, harvested from four different type of sensors. For fusion we propose a hybrid Convolutional Neural Network and Long Short-Term Memory (hybrid CNN+LSTM) and Multi-stream CNN. The classification results are compared to single modality data using Single-stream CNN, a state-of-the-art Vision Transformer, and statistical classifiers algorithms. The fusion models outperformed the single modality methods and classified gait speed of previously unseen 10 random subjects with 97% F1-score prediction accuracy of the four gait speed classes.
{"title":"Fusion from Multimodal Gait Spatiotemporal Data for Human Gait Speed Classifications","authors":"Abdullah S. Alharthi, K. Ozanyan","doi":"10.1109/SENSORS47087.2021.9639816","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639816","url":null,"abstract":"Human gait pattens remain largely undefined when relying on a single sensing modality. We report a pilot implementation of sensor fusion to classify gait spatiotemporal signals, from a publicly available dataset of 50 participants, harvested from four different type of sensors. For fusion we propose a hybrid Convolutional Neural Network and Long Short-Term Memory (hybrid CNN+LSTM) and Multi-stream CNN. The classification results are compared to single modality data using Single-stream CNN, a state-of-the-art Vision Transformer, and statistical classifiers algorithms. The fusion models outperformed the single modality methods and classified gait speed of previously unseen 10 random subjects with 97% F1-score prediction accuracy of the four gait speed classes.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"49 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83307135","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639813
Jianlin Chen, T. Tsukamoto, G. Langfelder, Shuji Tanaka
This paper reports a novel tuning fork type 2-axis dual mass resonator with frequency and Q-factor tuning mechanisms. The resonator consists of two proof masses and extra small masses in between them, which could tune the Q-factor of each axis independently with small effect on the frequency. The experimental results showed the as-fabricated frequency and the Q-factor mismatches were 5% and 7.7%, respectively. Q-factor tuning up to 21% could be reached by changing the squeeze film damping through mode coupling induced by suitably applied DC bias. The parasitic effect on frequency shift was as small as 810 ppm. Combining with the conventional frequency tuning method by the DC bias on the main proof masses, both the frequency and Q-factor could be tuned simultaneously. Finally, the frequency matching under 10 ppm and the Q-factor matching under 650 ppm could be achieved.
{"title":"Frequency and Quality Factor Matched 2-Axis Dual Mass Resonator","authors":"Jianlin Chen, T. Tsukamoto, G. Langfelder, Shuji Tanaka","doi":"10.1109/SENSORS47087.2021.9639813","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639813","url":null,"abstract":"This paper reports a novel tuning fork type 2-axis dual mass resonator with frequency and Q-factor tuning mechanisms. The resonator consists of two proof masses and extra small masses in between them, which could tune the Q-factor of each axis independently with small effect on the frequency. The experimental results showed the as-fabricated frequency and the Q-factor mismatches were 5% and 7.7%, respectively. Q-factor tuning up to 21% could be reached by changing the squeeze film damping through mode coupling induced by suitably applied DC bias. The parasitic effect on frequency shift was as small as 810 ppm. Combining with the conventional frequency tuning method by the DC bias on the main proof masses, both the frequency and Q-factor could be tuned simultaneously. Finally, the frequency matching under 10 ppm and the Q-factor matching under 650 ppm could be achieved.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"152 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85390426","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639814
Jing Liang, Shan Liang, Hao Zhang, Li Ma
The accurately estimation of leak parameter is an essential part of pipeline leakage risk assessment and guarantee of the quality of leak samples, while today’s estimation methods leverage only coarse time estimation and rely on expert experience. This paper presents a leak time parameter estimation framework fusing leak behavior and data distribution characteristic from gas flow sensor data. Under the proposed framework, a leak behavior and a data distribution characteristic extraction modules are established for guaranteeing automatic estimation of leakage time parameters in a fine-grained time range. The estimation of leak starting time for 69 different leak events are implemented based on gas flow sensors data. The estimation accuracy of 94.2% demonstrates the effectiveness of the proposed method.
{"title":"Estimation of Leak Time Parameter Based on Fusion of Leak Behavior and Data Distribution Characteristic from Gas Flow Sensor Data","authors":"Jing Liang, Shan Liang, Hao Zhang, Li Ma","doi":"10.1109/SENSORS47087.2021.9639814","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639814","url":null,"abstract":"The accurately estimation of leak parameter is an essential part of pipeline leakage risk assessment and guarantee of the quality of leak samples, while today’s estimation methods leverage only coarse time estimation and rely on expert experience. This paper presents a leak time parameter estimation framework fusing leak behavior and data distribution characteristic from gas flow sensor data. Under the proposed framework, a leak behavior and a data distribution characteristic extraction modules are established for guaranteeing automatic estimation of leakage time parameters in a fine-grained time range. The estimation of leak starting time for 69 different leak events are implemented based on gas flow sensors data. The estimation accuracy of 94.2% demonstrates the effectiveness of the proposed method.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88834057","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639671
E. Svendsen, M. Føre, L. Randeberg, J. A. Alfredsen
Accessing the welfare status of animals in salmon farming is a challenge due to the large production scale and limited diversity of technologies available to quantify individuals’ behavioural and physiological parameters. To increase technology diversity and facilitate rapid testing of future biosensing implants for farmed fish, we have designed and implemented a reusable and retoolable implant. The implant (9.4g, 13mm diameter × 47mm length) consists of a primary module with a basic sensor suite measuring linear accelerations, rotational rates, compass heading, temperature and magnetic field strength, and a user-defined secondary module featuring a biosensor measuring heart rate and changes in tissue perfusion (photoplethysmography) enabling estimation of blood oxygen saturation (SpO2). In this study, we describe the hardware (HW) and software (SW) of this sensor device and outline how it can be used to collect different data types from free-swimming farm fish in research and production settings. Finally, we discuss how this platform could be used as a tool for realising precision farming methods to improve animal welfare in aquaculture.
{"title":"Design of a novel biosensor implant for farmed Atlantic salmon (Salmo salar)","authors":"E. Svendsen, M. Føre, L. Randeberg, J. A. Alfredsen","doi":"10.1109/SENSORS47087.2021.9639671","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639671","url":null,"abstract":"Accessing the welfare status of animals in salmon farming is a challenge due to the large production scale and limited diversity of technologies available to quantify individuals’ behavioural and physiological parameters. To increase technology diversity and facilitate rapid testing of future biosensing implants for farmed fish, we have designed and implemented a reusable and retoolable implant. The implant (9.4g, 13mm diameter × 47mm length) consists of a primary module with a basic sensor suite measuring linear accelerations, rotational rates, compass heading, temperature and magnetic field strength, and a user-defined secondary module featuring a biosensor measuring heart rate and changes in tissue perfusion (photoplethysmography) enabling estimation of blood oxygen saturation (SpO2). In this study, we describe the hardware (HW) and software (SW) of this sensor device and outline how it can be used to collect different data types from free-swimming farm fish in research and production settings. Finally, we discuss how this platform could be used as a tool for realising precision farming methods to improve animal welfare in aquaculture.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"43 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81366228","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639748
P. Ahmmed, Timothy R. N. Holder, Marc Foster, I. Castro, Aakash Patel, T. Torfs, A. Bozkurt
Canine-assisted interactions have enormous potential in coping with psychological disorders and stress. It has been actively used for improving the mood of hospitalized patients, especially those suffering from chronic diseases like cancer. However, little progress has been made to enable the assessment of these interactions between the patient and the animal in a quantitative and undisruptive way. In this paper, we present a capacitively coupled biopotential recording system custom-designed for animal-human dyads. This system uses noncontact electrodes to monitor the heart rate and its variability to evaluate the physiological basis of the animal-assisted therapies. Preliminary in vivo evaluation of the system in humans and canines demonstrates promising measurement accuracy. The mean absolute error of the estimated heart rate was less than 0.25 BPM in reference to a commercial electrocardiography device. The future integration of this system into ergonomic form factors could enable a better understanding of animal-human interactions during canine-assisted therapy sessions by realizing an unobtrusive and continuous monitoring platform.
{"title":"Noncontact Electrophysiology Monitoring Systems for Assessment of Canine-Human Interactions","authors":"P. Ahmmed, Timothy R. N. Holder, Marc Foster, I. Castro, Aakash Patel, T. Torfs, A. Bozkurt","doi":"10.1109/SENSORS47087.2021.9639748","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639748","url":null,"abstract":"Canine-assisted interactions have enormous potential in coping with psychological disorders and stress. It has been actively used for improving the mood of hospitalized patients, especially those suffering from chronic diseases like cancer. However, little progress has been made to enable the assessment of these interactions between the patient and the animal in a quantitative and undisruptive way. In this paper, we present a capacitively coupled biopotential recording system custom-designed for animal-human dyads. This system uses noncontact electrodes to monitor the heart rate and its variability to evaluate the physiological basis of the animal-assisted therapies. Preliminary in vivo evaluation of the system in humans and canines demonstrates promising measurement accuracy. The mean absolute error of the estimated heart rate was less than 0.25 BPM in reference to a commercial electrocardiography device. The future integration of this system into ergonomic form factors could enable a better understanding of animal-human interactions during canine-assisted therapy sessions by realizing an unobtrusive and continuous monitoring platform.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"18 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78454809","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639760
K. Jayanand, A. Kaul
In this study, we present the study of van der Waal (vdW) hybrid structure of zero-dimensional graphene quantum dots (GQDs) and fullerene (C60) in conjunction with two-dimensional molybdenum disulphide (MoS2) and tungsten diselenide (WSe2) semiconducting transition metal dichalcogenides (TMDs). The vdW hybrid structures were characterized by Raman and photoluminescence (PL) spectroscopy which have revealed n-type doping occurring in MoS2 after decoration of GQDs, and a p-type doping occurring in WSe2 after decoration of C60. After outlining the fabrication of our 0D-2D heterostructure photodetectors, their photo response to incoming radiation was measured using broadband illumination which has revealed a two-fold increase in photo response in MoS2 and a three-fold increase in WSe2 after decoration with GQDs and C60, respectively. Our results presented here provide insights into 2D and 0D-based hybrid structures, thus enabling platform for high-performance photodetectors.
{"title":"Photoconductivity enhancement in MoS2 and WSe2 hybrids aided by light-absorbing carbon-based zero-dimensional quantum dots","authors":"K. Jayanand, A. Kaul","doi":"10.1109/SENSORS47087.2021.9639760","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639760","url":null,"abstract":"In this study, we present the study of van der Waal (vdW) hybrid structure of zero-dimensional graphene quantum dots (GQDs) and fullerene (C60) in conjunction with two-dimensional molybdenum disulphide (MoS2) and tungsten diselenide (WSe2) semiconducting transition metal dichalcogenides (TMDs). The vdW hybrid structures were characterized by Raman and photoluminescence (PL) spectroscopy which have revealed n-type doping occurring in MoS2 after decoration of GQDs, and a p-type doping occurring in WSe2 after decoration of C60. After outlining the fabrication of our 0D-2D heterostructure photodetectors, their photo response to incoming radiation was measured using broadband illumination which has revealed a two-fold increase in photo response in MoS2 and a three-fold increase in WSe2 after decoration with GQDs and C60, respectively. Our results presented here provide insights into 2D and 0D-based hybrid structures, thus enabling platform for high-performance photodetectors.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"6 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78738359","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9722610
Rui Ma, Guangyao Ding, Qi Hao
High-resolution camera arrays have been used in wide area and long distance surveillance as well as event recording. However, processing and storing the huge video streams of camera arrays remain a heavy burden for many applications. This paper presents a compressive object detection framework to reduce the camera data volume, and to accelerate the detection using high-resolution images with small performance degradation. The proposed method superimposes multiple images from different sub-cameras of the array, and performs detection on the superimposed data using neural networks. Detected bounding boxes are then relocated on the original sub-images as candidates which are further verified through target classification. The system only stores the high-resolution superimposed data and the low-resolution wide FOV images, which can guarantee the detection accuracy with smaller data volume. The proposed methods are validated using pedestrian datasets and real camera array images in terms of detection accuracy.
{"title":"Compressive Detection for Camera Array Images","authors":"Rui Ma, Guangyao Ding, Qi Hao","doi":"10.1109/SENSORS47087.2021.9722610","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9722610","url":null,"abstract":"High-resolution camera arrays have been used in wide area and long distance surveillance as well as event recording. However, processing and storing the huge video streams of camera arrays remain a heavy burden for many applications. This paper presents a compressive object detection framework to reduce the camera data volume, and to accelerate the detection using high-resolution images with small performance degradation. The proposed method superimposes multiple images from different sub-cameras of the array, and performs detection on the superimposed data using neural networks. Detected bounding boxes are then relocated on the original sub-images as candidates which are further verified through target classification. The system only stores the high-resolution superimposed data and the low-resolution wide FOV images, which can guarantee the detection accuracy with smaller data volume. The proposed methods are validated using pedestrian datasets and real camera array images in terms of detection accuracy.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"137 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76762939","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639640
V. Palaniappan, X. Zhang, D. Maddipatla, B. B. Narakathu, B. Bazuin, M. Atashbar
A vertical contact-separation mode (CS-mode) triboelectric nanogenerator (TENG) was developed for energy harvesting applications. The TENG was fabricated using polytetrafluoroethylene (PTFE) and Polyimide (kapton) films as negative and positive triboelectric layers, respectively and copper tape was used as top and bottom electrodes. Polydimethylsiloxane (PDMS) was used as a support layer to reduce the compression modulus of kapton and to improve the contact stress between kapton and PTFE, resulting in an enhanced TENG output voltage. The stress, strain, and compression modulus of triboelectric layers at three different conditions: bare kapton as tribo-layer (Case-1), bare PDMS as tribo-layer (Case-2), and PDMS/kapton as tribo-layer (Case-3) were modeled using COMSOL software. The voltage generation performance of TENG (Case 1, 2, and 3) was demonstrated by investigating the open-circuit voltage (OCV) at an applied mechanical force of 5 N. A maximum OCV of 11 Vp-p was measured for TENG with PDMS as support layer and a lower compression modulus of 8 MPa when compared to the TENG without PDMS (OCV of 2 Vp-p).
{"title":"Enhanced TENG Performance by Engineering the Compression Modulus of Triboelectric Layers","authors":"V. Palaniappan, X. Zhang, D. Maddipatla, B. B. Narakathu, B. Bazuin, M. Atashbar","doi":"10.1109/SENSORS47087.2021.9639640","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639640","url":null,"abstract":"A vertical contact-separation mode (CS-mode) triboelectric nanogenerator (TENG) was developed for energy harvesting applications. The TENG was fabricated using polytetrafluoroethylene (PTFE) and Polyimide (kapton) films as negative and positive triboelectric layers, respectively and copper tape was used as top and bottom electrodes. Polydimethylsiloxane (PDMS) was used as a support layer to reduce the compression modulus of kapton and to improve the contact stress between kapton and PTFE, resulting in an enhanced TENG output voltage. The stress, strain, and compression modulus of triboelectric layers at three different conditions: bare kapton as tribo-layer (Case-1), bare PDMS as tribo-layer (Case-2), and PDMS/kapton as tribo-layer (Case-3) were modeled using COMSOL software. The voltage generation performance of TENG (Case 1, 2, and 3) was demonstrated by investigating the open-circuit voltage (OCV) at an applied mechanical force of 5 N. A maximum OCV of 11 Vp-p was measured for TENG with PDMS as support layer and a lower compression modulus of 8 MPa when compared to the TENG without PDMS (OCV of 2 Vp-p).","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"1134 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76804894","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639539
Pixi Kang, Xiangyu Li
This paper presents a lightweight online semi-supervised learning algorithm that utilizes the sequentially arrived unlabeled user gesture samples to improve recognition performance for ultrasonic gesture recognition systems. For each class of gesture, a binary extreme random forest is pre-trained in the offline manner, by which the newly arrived unlabeled samples are given pseudo-labels before they are filtered according to probability and then fed to an initially unexpanded forest to grow its trees incrementally. To limit complexity, feature cache and index pool are introduced. Experiments show that the incrementally grown forests identify 8 gestures and 4 micro gestures with average accuracies of 95.8% and 93.6%, respectively exceeding its nearest offline supervised competitor by 2.1% and 4.1%.
{"title":"Lightweight Online Semi-Supervised Learning Algorithm for Ultrasonic Gesture Recognition","authors":"Pixi Kang, Xiangyu Li","doi":"10.1109/SENSORS47087.2021.9639539","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639539","url":null,"abstract":"This paper presents a lightweight online semi-supervised learning algorithm that utilizes the sequentially arrived unlabeled user gesture samples to improve recognition performance for ultrasonic gesture recognition systems. For each class of gesture, a binary extreme random forest is pre-trained in the offline manner, by which the newly arrived unlabeled samples are given pseudo-labels before they are filtered according to probability and then fed to an initially unexpanded forest to grow its trees incrementally. To limit complexity, feature cache and index pool are introduced. Experiments show that the incrementally grown forests identify 8 gestures and 4 micro gestures with average accuracies of 95.8% and 93.6%, respectively exceeding its nearest offline supervised competitor by 2.1% and 4.1%.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88615134","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 : 2021-10-31DOI: 10.1109/SENSORS47087.2021.9639839
H. Tabrizi, Saghi Forouhi, Omid Farhanieh, S. Magierowski, E. Ghafar-Zadeh
CMOS capacitive sensors for label-free monitoring of biological/chemical reactions are typically prone to inaccuracies due to the parasitic elements and mismatch rooted in the CMOS fabrication process as well as real-time changes inside the sample solution. The former can usually be compensated by employing differential circuits and static calibration of the sensor before the experiment. On the other hand, changes in the sample solution such as sedimentation of non-target molecules or change in the conductivity of solution can significantly alter the operating point and result in inaccurate sensor readings that might require recalibration of the sensor during the experiment. In this paper, we present a CMOS capacitive sensor that is calibration-free via the creation of time-resolved three-dimensional (3D) surface electrochemical profiles. These 3D profiles uncover the variations of both target and unwanted parasitic capacitances. The sensor includes on-chip interdigitated electrodes (IDEs), a wide input dynamic range (IDR) differential capacitance-to-current converter, a digitally programable reference capacitor, and an oscillator-based analog-to-digital converter (ADC), and is implemented using 0.35 µm AMS CMOS process. The IDR covers a change in capacitance as small as 1 fF up to 1.27 pF with a minimum detectable change of 0.416 fF.
{"title":"A Novel Calibration-Free Fully Integrated CMOS Capacitive Sensor for Life Science Applications","authors":"H. Tabrizi, Saghi Forouhi, Omid Farhanieh, S. Magierowski, E. Ghafar-Zadeh","doi":"10.1109/SENSORS47087.2021.9639839","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639839","url":null,"abstract":"CMOS capacitive sensors for label-free monitoring of biological/chemical reactions are typically prone to inaccuracies due to the parasitic elements and mismatch rooted in the CMOS fabrication process as well as real-time changes inside the sample solution. The former can usually be compensated by employing differential circuits and static calibration of the sensor before the experiment. On the other hand, changes in the sample solution such as sedimentation of non-target molecules or change in the conductivity of solution can significantly alter the operating point and result in inaccurate sensor readings that might require recalibration of the sensor during the experiment. In this paper, we present a CMOS capacitive sensor that is calibration-free via the creation of time-resolved three-dimensional (3D) surface electrochemical profiles. These 3D profiles uncover the variations of both target and unwanted parasitic capacitances. The sensor includes on-chip interdigitated electrodes (IDEs), a wide input dynamic range (IDR) differential capacitance-to-current converter, a digitally programable reference capacitor, and an oscillator-based analog-to-digital converter (ADC), and is implemented using 0.35 µm AMS CMOS process. The IDR covers a change in capacitance as small as 1 fF up to 1.27 pF with a minimum detectable change of 0.416 fF.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88729866","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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