2021 IEEE Sensors Applications Symposium (SAS)最新文献_第7页

Positional feedback of a linear track slider using a low-cost stretch sensor 使用低成本拉伸传感器的线性轨迹滑块的位置反馈

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530024

G. Olson, C. Davies, G. S. Gupta, Rose Davies, L. Fullard

引用次数: 0

IoT Multi-Hop Facilities via LoRa Modulation and LoRa WanProtocol within Thin Linear Networks 薄线性网络中基于LoRa调制和LoRa WanProtocol的IoT多跳设施

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530117

Federico Basili, Stefano Parrino, G. Peruzzi, A. Pozzebon

{"title":"IoT Multi-Hop Facilities via LoRa Modulation and LoRa WanProtocol within Thin Linear Networks","authors":"Federico Basili, Stefano Parrino, G. Peruzzi, A. Pozzebon","doi":"10.1109/SAS51076.2021.9530117","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530117","url":null,"abstract":"This paper proposes a novel network architecture integrating a multi-hop Long Range (LoRa)-based thin linear network within a LoRa Wide Area Network (LoRaWAN) infrastructure, with the aim of proposing linear distributed measurement systems forwarding their collected data to a LoRaWAN server by means of a hybrid LoRa-LoRaWAN node. Such device is able to collect LoRa packets coming from the linear network and to encapsulate them in LoRaWAN packets transmitted to the remote server by means of standard LoRaWAN Gateways. The operation of the nodes is regulated by an ad-hoc routing protocol which aims at minimizing their active period, in order to reduce their power consumption increasing the overall system lifetime. Similarly, the synchronization of the nodes aims at increasing the robustness of the network reducing at minimum packet losses. The effectiveness of the proposed network architecture in terms of successful packet deliveries and reduction of active time is tested in different configurations, exploiting 2-node, 3-node and 4-node chains as well as adopting increasingly larger cycle periods. Results show that the proposed configuration ensures a noteworthy robustness in terms of packets delivery while maintaining the duty-cycling at levels that may guarantee long life times and autonomous operation to the overall infrastructure.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128851502","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

Non-destructive evaluation of treated polyethylene terephthalate films by fluorescence lifetime imaging 荧光寿命成像无损评价处理过的聚对苯二甲酸乙二醇酯薄膜

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530008

M. Wohlschläger, M. Versen, C. Laforsch

引用次数: 1

A novel energy harvesting actuator for self-powered environmental sensors 一种用于自供电环境传感器的新型能量收集执行器

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530184

J. Curry, N. Harris, N. White

引用次数: 2

Partial Discharge Detection Using Distributed Acoustic Sensing at the Oil-Pressboard Interface 基于分布式声传感的油压界面局部放电检测

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530118

Laurie Kirkcaldy, P. Lewin, G. Lees, Rosalie Rogers

引用次数: 2

Noncontact Neonatal Respiration Rate Estimation Using Machine Vision 非接触新生儿呼吸率估计使用机器视觉

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530013

Daniel G. Kyrollos, J. Tanner, K. Greenwood, J. Harrold, J. Green

{"title":"Noncontact Neonatal Respiration Rate Estimation Using Machine Vision","authors":"Daniel G. Kyrollos, J. Tanner, K. Greenwood, J. Harrold, J. Green","doi":"10.1109/SAS51076.2021.9530013","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530013","url":null,"abstract":"Using video data of neonates admitted to the neonatal intensive care unit (NICU) we developed and compared the performance of various techniques for noncontact respiration rate (RR) estimation. Data were collected from an overhead colour and depth (RGB-D) camera, while gold standard physiologic data were captured from the hospital's patient monitor. We developed a deep learning algorithm for automatic detection of the face and chest area of the neonate. We then use this algorithm to identify time periods with low patient motion and to locate regions of interest for RR estimation. We produce a respiration signal by quantifying the chest movement using the raw RGB video, motion-magnified RGB video, and depth video. We compare this to a respiration signal derived from the changes in the green channel of the face. We were able to estimate RR from motion-magnified video and depth video, achieving a mean absolute error of less than 3.5 BPM for 69% and 67% of the time for each stream, respectively. We achieve this result without the need for skin segmentation and can apply our technique to fully clothed neonatal patients. We show that similar performance can be achieved using the depth and colour stream using this technique.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128117356","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: 12

Tackling Time-Variability in sEMG-based Gesture Recognition with On-Device Incremental Learning and Temporal Convolutional Networks 用设备上增量学习和时间卷积网络解决基于表面肌电信号的手势识别中的时变问题

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530007

A. Burrello, Marcello Zanghieri, Cristian Sarti, Leonardo Ravaglia, Simone Benatt, L. Benini

{"title":"Tackling Time-Variability in sEMG-based Gesture Recognition with On-Device Incremental Learning and Temporal Convolutional Networks","authors":"A. Burrello, Marcello Zanghieri, Cristian Sarti, Leonardo Ravaglia, Simone Benatt, L. Benini","doi":"10.1109/SAS51076.2021.9530007","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530007","url":null,"abstract":"Human-machine interaction is showing promising results for robotic prosthesis control and rehabilitation. In these fields, hand movement recognition via surface electromyographic (sEMG) signals is one of the most promising approaches. However, it still suffers from the issue of sEMG signal's variability over time, which negatively impacts classification robustness. In particular, the non-stationarity of input signals and the surface electrodes' shift can cause up to 30 % degradation in gesture recognition accuracy. This work addresses the temporal variability of the sEMG-based gesture recognition by proposing to train a Temporal Convolutional Network (TCN) incrementally over multiple gesture training sessions. Using incremental learning, we re-train our model on stored latent data spanning multiple sessions. We validate our approach on the UniBo-20-Session dataset, which includes 8 hand gestures from 3 subjects. Our incremental learning framework obtains 18.9% higher accuracy compared to a baseline with a standard single training session. Deploying our TCN on a Parallel, Ultra-Low Power (PULP) microcontroller unit (MCU), GAP8, we achieve an inference latency and energy of 12.9 ms and 0.66 mJ, respectively, with a weight memory footprint of 427 kB and a data memory footprint of 0.5-32 MB.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129677988","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

Parallel Delta-Sigma ADC modulation for performance increase of position sensors in industrial applications 用于提高工业应用中位置传感器性能的并行Delta-Sigma ADC调制

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-08-23 DOI: 10.1109/SAS51076.2021.9530099

Stefan Höltl, Matthias Kneißl, M. Versen

引用次数: 0

SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications 智能手:面向假肢和机器人应用的嵌入式智能手

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2021-07-23 DOI: 10.1109/SAS51076.2021.9530050

Xiaying Wang, Fabian Geiger, Vlad Niculescu, M. Magno, L. Benini

{"title":"SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications","authors":"Xiaying Wang, Fabian Geiger, Vlad Niculescu, M. Magno, L. Benini","doi":"10.1109/SAS51076.2021.9530050","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530050","url":null,"abstract":"The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment. Robotic and prosthetic devices lack refined tactile feedback from their end-effectors, leading to counterintuitive and complex control strategies. To address this lack, tactile sensors have been designed and developed, but they are either expensive and not scalable or offer an insufficient spatial and temporal resolution. This paper focuses on overcoming these issues by designing a smart embedded system, called SmartHand, enabling the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multi-sensor array for prosthetic and robotic applications. We acquire a new tactile dataset consisting of 340,000 frames while interacting with 16 objects from everyday life and the empty hand, i.e., a total of 17 classes. The design of the embedded system minimizes response latency in classification, by deploying a small yet accurate convolutional neural network on a high-performance ARM Cortex-M7 microcontroller. Compared to related work, our model requires one order of magnitude less memory and 15.6 x fewer computations while achieving similar inter-session accuracy and up to 98.86% and 99.83% top-1 and top-3 cross-validation accuracy, respectively. Experimental results of the designed prototype show a total power consumption of 505mW and a latency of only 100ms.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124337379","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

A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images 从整张幻灯片图像中提取Gleason组织和前列腺癌分级的扩展残差分层分割框架

2021 IEEE Sensors Applications Symposium (SAS) Pub Date : 2020-11-01 DOI: 10.1109/SAS51076.2021.9530155

Taimur Hassan, Bilal Hassan, A. El-Baz, N. Werghi

引用次数: 10