2022 IEEE Sensors Applications Symposium (SAS)最新文献

An Anomaly Detection System for Transparent Objects Using Polarized-Image Fusion Technique 基于偏振图像融合技术的透明物体异常检测系统

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881251

Lixing Yu, Atsutake Kosuge, M. Hamada, T. Kuroda

{"title":"An Anomaly Detection System for Transparent Objects Using Polarized-Image Fusion Technique","authors":"Lixing Yu, Atsutake Kosuge, M. Hamada, T. Kuroda","doi":"10.1109/SAS54819.2022.9881251","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881251","url":null,"abstract":"An anomaly detection system using a polarized-image fusion technique has been developed for food inspection applications. It is capable of detecting (a) foreign objects among objects wrapped in transparent reflective material and (b) transparent foreign objects in transparent bottles. The conventional anomaly detection system using a traditional RGB camera has low accuracy for such detection, due to the large amount of glare that can occur from reflective surfaces. Regions with glare are often falsely perceived as anomalies. Since transparent foreign objects have few features, they are difficult to recognize. To address these problems, a polarized-image fusion (PIF) technique is developed. Four polarized images are fused to synthesize a high-quality image where glare is suppressed, and transparent foreign objects are highlighted. These polarized images are captured simultaneously by a single camera utilizing an advanced polarized CMOS image sensor. The PIF technique was evaluated with two kinds of data set: (1) cookie samples wrapped in transparent plastic bags and (2) transparent plastic bottles containing transparent plastic foreign objects. High anomaly detection accuracies of 0.851 AUC (area under receiver operating characteristic curve) for the cookie sample data set and 0.871 AUC for the plastic bottle data set were achieved. Compared with the deep one-class classification neural network with simple RGB data input, the accuracies were improved by 0.09 AUC for both cases.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123306048","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: 3

Metrological Analysis of a Contactless Inductive Position Measurement System 一种非接触电感式位置测量系统的计量分析

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881383

S. Tagger, M. Neumayer, G. Gruber, H. Wegleiter

引用次数: 2

Magnetic Sensor Array for Determining the Assembly Torsion and Preload of a Bolted Joint 用于确定螺栓连接总成扭矩和预紧力的磁传感器阵列

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881362

Thorben Schuthe, K. Riemschneider, A. Meyer-Eschenbach

引用次数: 0

Modeling of a Bacterial Cellulose-based Composite in Bending Configuration 弯曲构型细菌纤维素基复合材料的建模

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881353

R. Caponetto, Andrea Cincotta, G. Pasquale, S. Graziani, A. Pollicino, C. Trigona

引用次数: 0

A Bending Angle Sensor Based on Magnetic Coupling Suitable for Soft Robotic Finger 一种适用于柔性机器人手指的磁耦合弯曲角度传感器

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881348

Debasrita Kar, B. George, K. Sridharan

{"title":"A Bending Angle Sensor Based on Magnetic Coupling Suitable for Soft Robotic Finger","authors":"Debasrita Kar, B. George, K. Sridharan","doi":"10.1109/SAS54819.2022.9881348","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881348","url":null,"abstract":"Sensing the angle of bending of a soft robotic finger is valuable in many applications but it is a non-trivial task. In this paper, a simple but effective sensing approach based on variable mutual inductance is presented to sense the bending angle. Existing inductive bend sensors use concentric coil based designs and they are not easy to manufacture and integrate into the finger. The planar coil based bend sensor, proposed in this work, is integrated inside the soft robotic finger. The sensor comprises of two flexible printed circuit boards based spiral coils that are magnetically coupled. Three such units are proposed to use in a finger. Each set of planar coils is arranged in such a manner that they give a measure of the curvature of the finger. It can give localized bending of the corresponding parts of the structure which is useful if the bending is not uniform. The measurement circuit required for the sensor is very simple. A prototype sensor built and tested showed a repeatable input-output characteristic with a repeatability error of 0.6%. The proposed sensor does not use the grasping area, is easy to integrate and less expensive. The output of the sensor is immune to moisture, dust and oil.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068375","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

Rx position effect on Device Free Indoor Localization in the 28 GHz band Rx位置对28ghz频段设备自由室内定位的影响

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881244

Verónica Ojeda, Juan C. Aviles

引用次数: 2

Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data 从原始超声数据中自动提取肌束夹角

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881341

Soley Hafthorsdottir, S. Vostrikov, A. Cossettini, Michael Rieder, Christoph Leitner, M. Magno, L. Benini

{"title":"Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data","authors":"Soley Hafthorsdottir, S. Vostrikov, A. Cossettini, Michael Rieder, Christoph Leitner, M. Magno, L. Benini","doi":"10.1109/SAS54819.2022.9881341","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881341","url":null,"abstract":"Compact, wearable, wireless ultrasound (US) sensing systems are promising devices for the observation of human muscle dynamics, offering low power, non-invasive continuous monitoring of contracting muscles. Plane wave imaging is an ideal imaging modality to meet the high frame rate requirements of fast contracting muscles. However, the low power requirements of wearable wireless US constrain the data transfer rates from the probe to the host computer, and limit the maximum number of transducer channels, at the same time discouraging beamforming on the probe. Therefore, it is crucial to extract physiological parameters directly from raw US data for applications demanding fast imaging speeds (like monitoring muscles and tendons in motion). Machine Learning (ML) methods can be employed to effectively extract such features. Although a few recent works demonstrated A-mode US for motion and force prediction, the automatic extraction of structural muscle features from raw data is still in its infancy. This paper demonstrates the feasibility of extracting pennation angles from raw US data on a small dataset of contracting medial gastrocnemius muscles. Automatically extracted labels from US images are used as ground truth to train ML algorithms that predict pennation angles directly from raw US data, without the need for image reconstruction. We employ statistical features, Principle Components Analysis (PCA) and Covolutional Autoencoder (AE) for feature extraction and evaluate Random Forest (RF), Gradient Boosting (XGBoost) and Convoltional Neural Network (CNN) as regressors. Experimental results show that the best method (AE + XGBoost) achieves a mean absolute error of ~ 0.43° that is consistent with the variability of the manually annotated pennation angles reported in the literature, with a memory footprint smaller than 400 kB and less than 5 ms execution time.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114580619","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

Printed wireless battery-free sensor tag for structural health monitoring of natural fiber composites 用于天然纤维复合材料结构健康监测的印刷无线无电池传感器标签

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881368

Lukas Rauter, Johanna Zikulnig, L. Neumaier, L. Faller, H. Zangl, J. Kosel

引用次数: 2

Integration of fiber optic sensors in organ-on-a-chip devices towards label-free cell viability assays 将光纤传感器集成到器官芯片设备中，实现无标签细胞活力分析

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881355

Sanzhar Shakarim, D. Tosi, G. Kulsharova

引用次数: 0

Classification of batteries in waste streams using magnetic induction spectroscopy 利用磁感应光谱法对废物流中的电池进行分类

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881242

Kane C. Williams, M. O’Toole, L. A. Marsh, A. Peyton

{"title":"Classification of batteries in waste streams using magnetic induction spectroscopy","authors":"Kane C. Williams, M. O’Toole, L. A. Marsh, A. Peyton","doi":"10.1109/SAS54819.2022.9881242","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881242","url":null,"abstract":"Magnetic induction is widely used to detect and classify metal objects over a range of applications; this paper considers the potential of this technique to inspect for the presence and characteristics of batteries within waste streams. As the number of batteries used across the world increases, an efficient method is needed to ensure batteries can be classified to allow for more efficient recycling. The detection of batteries would also reduce the risk of fire and pollution by identifying the battery before they are crushed or shredded.In this study, a magnetic induction sensor measured the batteries and scrap metal between 781 Hz and 95282 Hz to allow a significant frequency range to be observed. The real component (in-phase) of a battery’s electromagnetic response is different from scrap metal; this could allow for an algorithm to be trained to detect batteries within metal waste or when inside non-metallic objects. The response observed shows that batteries could be grouped into size, which is useful if no line of sight is available, which a traditional camera system requires. Once grouped into size the batteries could be further separated according to their internal contents when the real component is used; this would reduce the risk of cross-contamination when they are recycled. The real component response of lithium and NiMH batteries is different when compared to the other batteries; this could allow them to be detected and removed from a waste stream, which is important as lithium batteries can set on fire.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123577565","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: 3