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Fully Printed pH Sensor Based in Carbon Black/Polyaniline Nanocomposite 基于炭黑/聚苯胺纳米复合材料的全印刷pH传感器
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639597
Shirin Mahinnezhad, H. Emami, Mohsen Ketabi, A. A. Shboul, Najet Belkhamssa, Andy Shih, R. Izquierdo
{"title":"Fully Printed pH Sensor Based in Carbon Black/Polyaniline Nanocomposite","authors":"Shirin Mahinnezhad, H. Emami, Mohsen Ketabi, A. A. Shboul, Najet Belkhamssa, Andy Shih, R. Izquierdo","doi":"10.1109/SENSORS47087.2021.9639597","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639597","url":null,"abstract":"In this work, a fully screen-printed and flexible potentiometric pH sensor was designed and fabricated by incorporating a carbon black (CB) paste/polyaniline emeraldine salt (PANI-ES) nanocomposite as the working electrode and Ag/AgCl as the quasi-reference electrode. Rather than the PANI electrochemical polymerization deposition method, the PANI-ES was blended with a commercial CB paste for screen-printing, enabling a fully printed and scalable process. As a result, a nanocomposite mixture of 99.1% CB and 0.9% PANI-ES emerged as a promising nanocomposite candidate to develop high-performance pH sensors. The sensor exhibited a near Nernstian sensitivity of 50 mV/pH, response time of 15 s at room temperature, high linearity in the pH range between 3 and 11 and reversible pH sensing performance. The sensing mechanism depends mainly on the degree of the oxidation states transition of PANI-ES at different pH levels. The proposed flexible pH sensor can be used to monitor a patient’s health and water quality.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"22 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":"84202597","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}
引用次数: 1
People Counting Solution Using an FMCW Radar with Knowledge Distillation From Camera Data 基于相机数据知识精馏的FMCW雷达人员计数解决方案
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639798
Michael Stephan, Souvik Hazra, Avik Santra, R. Weigel, Georg Fischer
{"title":"People Counting Solution Using an FMCW Radar with Knowledge Distillation From Camera Data","authors":"Michael Stephan, Souvik Hazra, Avik Santra, R. Weigel, Georg Fischer","doi":"10.1109/SENSORS47087.2021.9639798","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639798","url":null,"abstract":"Radar systems enable remote sensing of multiple persons within their field of view. In this paper, we propose a novel architecture to perform people counting using a 60 GHz Frequency Modulated Continuous Wave radar trained on supervised radar data and knowledge distillation performed using synchronized camera data. In the evaluation phase, only the radar encoder with Range - Doppler Images (RDI) as input is used and tested on a dataset consisting of scenarios recorded in a different setup than the training recordings with up to 6 persons present. In this paper we focus on showing the benefit of using the cross-modal camera information compared to the same unimodal model. In spite of the low-cost radar sensor, the proposed architecture achieves an accuracy of 71% compared to 58% for the test data from a different sensor with a different orientation and aspect angle, and an accuracy of 89% compared to 74% for test data from the same radar sensor when training without knowledge distillation.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"183 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":"86814645","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}
引用次数: 6
Calibration-free target detection based on thermal and distance sensor fusion 基于热与距离传感器融合的无标定目标检测
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639706
S. Kianoush, S. Savazzi, V. Rampa, L. Costa, Denis Tolochenko
{"title":"Calibration-free target detection based on thermal and distance sensor fusion","authors":"S. Kianoush, S. Savazzi, V. Rampa, L. Costa, Denis Tolochenko","doi":"10.1109/SENSORS47087.2021.9639706","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639706","url":null,"abstract":"Infrared (IR) thermal vision systems provide a passive and contact-less framework to evaluate temporal signatures of people presence in indoor scenarios. However, static 2D IR thermal projection of complex 3D objects cannot provide sufficient information for large-scale and continuous people estimation tasks. This paper proposes a change-point detection algorithm that jointly fuses thermal and distance information obtained from an IR array and an ultrasonic distance sensor to detect targets, namely human subjects, inside an indoor environment. An extensive validation phase has been carried out through experimental trials that have been conducted in a smart office using ceiling-mounted devices. Unlike previous works in this area, the proposed approach eliminates time consuming calibration steps by highlighting the benefits of the IR thermal and ultrasonic sensor fusion framework.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"73 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":"84268212","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}
引用次数: 2
Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar 传感器罩损伤对汽车激光雷达点云的影响
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639697
Birgit Schlager, T. Goelles, D. Watzenig
{"title":"Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar","authors":"Birgit Schlager, T. Goelles, D. Watzenig","doi":"10.1109/SENSORS47087.2021.9639697","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639697","url":null,"abstract":"Safe automated driving requires reliable perception sensors with low fault rates. Detecting perception sensor faults before path planning avoids fault propagation through the processing pipeline of automated vehicles. As the basis for further development of fault detection algorithms, the present work presents effects of damaged lidar sensor covers considering scratches, cracks, and holes. We used an automotive lidar, which provides point clouds, and calculated deviations between the lidar points on a target and an ideal plane representing the target to evaluate the effect of damaged covers. Results show that sensor cover damages have an effect on point cloud data.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"111 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":"80645032","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}
引用次数: 1
Detecting Charge Separation in Optoelectronic Materials and Devices Using Planar Microwave Resonators: An Overview 利用平面微波谐振器检测光电材料和器件中的电荷分离:综述
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639735
M. Rana, K. Shankar
{"title":"Detecting Charge Separation in Optoelectronic Materials and Devices Using Planar Microwave Resonators: An Overview","authors":"M. Rana, K. Shankar","doi":"10.1109/SENSORS47087.2021.9639735","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639735","url":null,"abstract":"The advent of high Q-factor planar microwave resonators has opened up new possibilities for the electronic characterization of semiconductor heterojunctions and hot carrier plasmonic devices. The high sensitivity of planar microwave resonators to changes in the complex permittivity of semiconductor materials placed in the resonator coupling gap allows detection of both mobile and trapped carriers. Therefore, long-lived excess carriers in relaxation-type semiconductors occurring either due to photoexcitation or hot carrier injection from a plasmonic sensitizer can be detected and quantified. Application of a large signal DC bias on top of the small-signal microwave bias further enables differentiation between trapped electrons and trapped holes. In most cases, characteristic lifetimes for trap-mediated processes can also be extracted.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"97 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":"77952914","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}
引用次数: 0
Enhanced gas sensing characteristics of metal doped WS2 nanoflowers 金属掺杂WS2纳米花增强气敏特性
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639580
Aanchal Alagh, F. Annanouch, E. Llobet
{"title":"Enhanced gas sensing characteristics of metal doped WS2 nanoflowers","authors":"Aanchal Alagh, F. Annanouch, E. Llobet","doi":"10.1109/SENSORS47087.2021.9639580","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639580","url":null,"abstract":"Herein, we demonstrate gas sensing characteristics of metal-decorated WS2 towards ammonia (NH3) gas detection at moderate temperature. Chemiresistive sensors are fabricated using a combination of chemical vapor deposition method with sputter deposition technique. WO3 nanowires grown via aerosol assisted chemical vapor deposition technique are sulfurized to form tungsten disulfide nanoflowers. The as grown nanoflowers are then decorated with metal nanoparticles of gold, silver and palladium using the sputtering technique at different deposition time and temperatures. The morphological and structural characteristics were studied using FE-SEM and Raman spectroscopy. The performance of the three fabricated sensors is compared towards NH3 gas detection at room and elevated temperatures. Results show that the synthesized material (WS2/Pd, WS2/Au, WS2/Ag) behaves as a p-type semiconductor towards NH3 gas. Also, the results of NH3 gas sensing characteristics demonstrated the promising effects of palladium decoration for the enhanced response in low-power consumption. Moreover, the room-temperature operation of the three sensors is discussed. Hence, this study demonstrates the realization of a high-performance NH3 gas sensor using a facile route along with low power consumption.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"47 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":"78408934","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}
引用次数: 0
Acoustic and Ultrasonic Characterisation of Blockages and Defects in Underground Pipes 地下管道堵塞和缺陷的声学和超声表征
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639823
Alex Towlson, Yicheng Yu, G. Sailor, K. Horoshenkov, A. Croxford, B. Drinkwater
{"title":"Acoustic and Ultrasonic Characterisation of Blockages and Defects in Underground Pipes","authors":"Alex Towlson, Yicheng Yu, G. Sailor, K. Horoshenkov, A. Croxford, B. Drinkwater","doi":"10.1109/SENSORS47087.2021.9639823","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639823","url":null,"abstract":"This paper explores sensing methodologies for the detection and characterisation of blockages in pipes by small robotic platforms. It proposes the use of low frequency acoustic measurements to detect blockages at range, while a higher frequency ultrasonic array is used to characterise them at closer range. The paper explores how these systems may be optimised for such a process, through the number of elements in both the acoustic and Ultrasonic array. This ensures that the developed sensing approach is suitable for application in this difficult environment.","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":"84919015","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}
引用次数: 1
ENFES: ENsemble FEw-Shot Learning For Intelligent Fault Diagnosis with Limited Data 基于有限数据的集成少镜头学习智能故障诊断
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639633
Onat Güngör, T. Rosing, Baris Aksanli
{"title":"ENFES: ENsemble FEw-Shot Learning For Intelligent Fault Diagnosis with Limited Data","authors":"Onat Güngör, T. Rosing, Baris Aksanli","doi":"10.1109/SENSORS47087.2021.9639633","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639633","url":null,"abstract":"Fault diagnosis is a key component of predictive system maintenance. Big data collected from sensors helps create data-driven fault diagnosis methods. However, it may be extremely costly to label specific fault types in a collected dataset. Hence, prediction algorithms should perform well under limited supervision. Few-shot learning (FSL) can provide a great prediction performance using very limited labeled data by discovering similarity among input pairs. But selection of a single FSL method may be arduous due to changing working conditions. Ensemble FSL solves this problem by combining a variety of FSL methods systematically. We propose an ensemble FSL framework, ENFES, where we combine 5 different Siamese neural network architectures using an iterative majority voting classifier. Our transfer learning-oriented experiments show that ENFES can improve the best algorithm significantly while using very limited labeled data. We obtain up to 16.4% improvement over the best algorithm by only using 0.3% of the training data.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"26 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":"87492202","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}
引用次数: 4
Adding Object Manipulation Capabilities to Social Robots by using 3D and RGB Cameras Data 通过使用3D和RGB相机数据为社交机器人添加对象操作功能
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639608
G. Mezzina, D. Venuto
{"title":"Adding Object Manipulation Capabilities to Social Robots by using 3D and RGB Cameras Data","authors":"G. Mezzina, D. Venuto","doi":"10.1109/SENSORS47087.2021.9639608","DOIUrl":"https://doi.org/10.1109/SENSORS47087.2021.9639608","url":null,"abstract":"This paper outlines the design and implementation of novel object manipulation for a social robot, here Pepper by SoftBank Robotics. It is primarily designed for verbal interaction and has therefore not been equipped with object manipulation capabilities. The proposed routine exploits the built-in RGB and 3D cameras. First, semantic segmentation based on the Mini-YOLOv3 neural network is run on the RGB image. Next, 3D sensor data are used to position the hand over the object, implementing a novel routine to grab the object and to scan it for recognition purposes. To preserve patient and location sensitive data, the here-proposed architecture operates automatically and offline, running on the robot’s operating system. Experimental results on 370 grabbing processes showed how the manipulation routine achieves a grabbing success rate of up to 96%. They also proved that the success rate remains unaltered if the target object is positioned in a rectangular area of ± 6 cm × ± 3 cm centered in the nominal position provided by an initial positioning grid. The grabbing success rate remains above 80% even if the object to be grabbed is stored with an angle that ranges between 10° and 45° within the above-reported area.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"707 ","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":"91449074","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}
引用次数: 0
Fusion from Multimodal Gait Spatiotemporal Data for Human Gait Speed Classifications 基于多模态步态时空数据的人类步态速度分类
2021 IEEE Sensors Pub Date : 2021-10-31 DOI: 10.1109/SENSORS47087.2021.9639816
Abdullah S. Alharthi, K. Ozanyan
{"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}
引用次数: 1
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