Pub Date : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00023
A. Trotta, Federico Montori, Giacomo Vallasciani, L. Bononi, M. D. Felice
Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.
{"title":"Optimizing IoT-based Human Activity Recognition on Extreme Edge Devices","authors":"A. Trotta, Federico Montori, Giacomo Vallasciani, L. Bononi, M. D. Felice","doi":"10.1109/SMARTCOMP58114.2023.00023","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00023","url":null,"abstract":"Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122144651","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00059
Ta-Ying Cheng
This extended abstract discusses the current methods and work progress on sampling large-scale point cloud datasets with semantics and reconstructing 3D objects from sparse inputs. In particular, we describe a proposed meta sampling strategy to quickly adapt sampling to multiple tasks and potential methods to improve multi-modal reconstruction. These methods could benefit immensely in creating in-depth situational awareness for challenging missions and rescues.
{"title":"Efficient 3D Feature Learning for Real-Time Awareness","authors":"Ta-Ying Cheng","doi":"10.1109/SMARTCOMP58114.2023.00059","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00059","url":null,"abstract":"This extended abstract discusses the current methods and work progress on sampling large-scale point cloud datasets with semantics and reconstructing 3D objects from sparse inputs. In particular, we describe a proposed meta sampling strategy to quickly adapt sampling to multiple tasks and potential methods to improve multi-modal reconstruction. These methods could benefit immensely in creating in-depth situational awareness for challenging missions and rescues.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906228","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00034
Ziran Min, S. Gokhale, Shashank Shekhar, C. Mahmoudi, Zhuangwei Kang, Yogesh D. Barve, A. Gokhale
Existing massive deployments of IoT devices in support of smart computing applications across a range of domains must leverage critical features of 5G, such as network slicing, to receive differentiated and reliable services. However, the voluminous, dynamic, and heterogeneous nature of IoT traffic imposes complexities on the problems of network flow classification, network traffic analysis, and accurate quantification of the network requirements, thereby making the provisioning of 5G network slices across the application mix a challenging problem. To address these needs, we propose a novel network traffic classification approach that consists of a pipeline that combines Principal Component Analysis (PCA), with KMeans clustering and Hellinger distance. PCA is applied as the first step to efficiently reduce the dimensionality of features while preserving as much of the original information as possible. This significantly reduces the runtime of KMeans, which is applied as the second step. KMeans, being an unsupervised approach, eliminates the need to label data which can be cumbersome, error-prone, and time-consuming. In the third step, a Hellinger distance-based recursive KMeans algorithm is applied to merge similar clusters toward identifying the optimal number of clusters. This makes the final clustering results compact and intuitively interpretable within the context of the problem, while addressing the limitations of traditional KMeans algorithm, such as sensitivity to initialization and the requirement of manual specification of the number of clusters. Evaluation of our approach on a real-world IoT dataset demonstrates that the pipeline can compactly represent the dataset as three clusters. The service properties of these clusters can be easily inferred and directly mapped to different types of slices in the 5G network.
{"title":"A Classification Framework for IoT Network Traffic Data for Provisioning 5G Network Slices in Smart Computing Applications","authors":"Ziran Min, S. Gokhale, Shashank Shekhar, C. Mahmoudi, Zhuangwei Kang, Yogesh D. Barve, A. Gokhale","doi":"10.1109/SMARTCOMP58114.2023.00034","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00034","url":null,"abstract":"Existing massive deployments of IoT devices in support of smart computing applications across a range of domains must leverage critical features of 5G, such as network slicing, to receive differentiated and reliable services. However, the voluminous, dynamic, and heterogeneous nature of IoT traffic imposes complexities on the problems of network flow classification, network traffic analysis, and accurate quantification of the network requirements, thereby making the provisioning of 5G network slices across the application mix a challenging problem. To address these needs, we propose a novel network traffic classification approach that consists of a pipeline that combines Principal Component Analysis (PCA), with KMeans clustering and Hellinger distance. PCA is applied as the first step to efficiently reduce the dimensionality of features while preserving as much of the original information as possible. This significantly reduces the runtime of KMeans, which is applied as the second step. KMeans, being an unsupervised approach, eliminates the need to label data which can be cumbersome, error-prone, and time-consuming. In the third step, a Hellinger distance-based recursive KMeans algorithm is applied to merge similar clusters toward identifying the optimal number of clusters. This makes the final clustering results compact and intuitively interpretable within the context of the problem, while addressing the limitations of traditional KMeans algorithm, such as sensitivity to initialization and the requirement of manual specification of the number of clusters. Evaluation of our approach on a real-world IoT dataset demonstrates that the pipeline can compactly represent the dataset as three clusters. The service properties of these clusters can be easily inferred and directly mapped to different types of slices in the 5G network.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"25 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120852810","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00068
L. D'Agati, F. Longo, Giovanni Merlino, A. Puliafito
The COVID-19 pandemic has emphasized the importance of responsive, efficient, and cost-effective production networks to manufacture essential goods rapidly. The Air Factories 2.0 project is a research-driven initiative designed to withstand the pandemic by leveraging advanced technologies, such as 3D printing, blockchains, and distributed manufacturing, to summon the maker community’s expertise and resources. However, this project’s potential extends beyond the medical field and can be actualized in several domains. This paper comprehensively analyzes the Air Factories 2.0 platform, its technological and scientific contexts, and its potential implications for the future of manufacturing, emergency response, and 3D printing-enabled decentralized supply chains.The study outlines the Air Factories 2.0 project’s operational and organizational structure, stakeholders and roles, participation and tokenization mechanisms, algorithms used to manage production and distribution, blockchains, and smart contracts employed to ensure transparency, security, and efficiency. Furthermore, this paper examines the advantages and limitations of the Air Factories 2.0 platform for the future of advanced manufacturing across various domains.
{"title":"3D Printing and Blockchains for an Emergency Response Supply Chain","authors":"L. D'Agati, F. Longo, Giovanni Merlino, A. Puliafito","doi":"10.1109/SMARTCOMP58114.2023.00068","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00068","url":null,"abstract":"The COVID-19 pandemic has emphasized the importance of responsive, efficient, and cost-effective production networks to manufacture essential goods rapidly. The Air Factories 2.0 project is a research-driven initiative designed to withstand the pandemic by leveraging advanced technologies, such as 3D printing, blockchains, and distributed manufacturing, to summon the maker community’s expertise and resources. However, this project’s potential extends beyond the medical field and can be actualized in several domains. This paper comprehensively analyzes the Air Factories 2.0 platform, its technological and scientific contexts, and its potential implications for the future of manufacturing, emergency response, and 3D printing-enabled decentralized supply chains.The study outlines the Air Factories 2.0 project’s operational and organizational structure, stakeholders and roles, participation and tokenization mechanisms, algorithms used to manage production and distribution, blockchains, and smart contracts employed to ensure transparency, security, and efficiency. Furthermore, this paper examines the advantages and limitations of the Air Factories 2.0 platform for the future of advanced manufacturing across various domains.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121393865","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00066
Doris E. M. Brown
Traditional strategic information design literature assumes receivers trust the signals shared by the sender, the sender and receivers have symmetric information at the outset of the interaction, and receivers update their beliefs according to Bayes rule. In our work, we consider an interaction between a smart navigation system and multiple drivers as a Stackelberg game within a traffic network in which the leader may perturb traffic information shared with selfish receivers to reach a system-optimal routing outcome that minimizes network congestion. We propose a framework that deviates from the traditional assumptions of the strategic information design framework to better mimic real-world human behavior and consider conditions under which a sender shares deceptive information with a receiver.
{"title":"Traffic Routing under Driver Distrust","authors":"Doris E. M. Brown","doi":"10.1109/SMARTCOMP58114.2023.00066","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00066","url":null,"abstract":"Traditional strategic information design literature assumes receivers trust the signals shared by the sender, the sender and receivers have symmetric information at the outset of the interaction, and receivers update their beliefs according to Bayes rule. In our work, we consider an interaction between a smart navigation system and multiple drivers as a Stackelberg game within a traffic network in which the leader may perturb traffic information shared with selfish receivers to reach a system-optimal routing outcome that minimizes network congestion. We propose a framework that deviates from the traditional assumptions of the strategic information design framework to better mimic real-world human behavior and consider conditions under which a sender shares deceptive information with a receiver.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151763","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00051
Nitesh Bharot, M. Soderi, Priyank Verma, J. Breslin
Reducing product failure rates is crucial to ensure a healthy production line. However, the current approach for inspecting product quality is inefficient, costly, and time-consuming, relying on manual inspection at the end of the production process. This research paper focuses on the utilization of transfer learning, an intelligent machine-learning technique, to improve the accuracy and efficiency of product quality inspection in production lines. The proposed approach utilizes transfer learning to adapt a pre-trained model from a related domain to the target domain, enabling accurate product quality prediction with limited data. The reference architecture provides a framework for implementing the proposed approach in a manufacturing environment, enabling real-time monitoring and decision-making based on product quality predictions. The proposed approach can improve the accuracy of faulty product detection by up to 11% compared to traditional techniques, as demonstrated by evaluations on a real-world production dataset.
{"title":"Improving Product Quality Control in Smart Manufacturing through Transfer Learning-Based Fault Detection","authors":"Nitesh Bharot, M. Soderi, Priyank Verma, J. Breslin","doi":"10.1109/SMARTCOMP58114.2023.00051","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00051","url":null,"abstract":"Reducing product failure rates is crucial to ensure a healthy production line. However, the current approach for inspecting product quality is inefficient, costly, and time-consuming, relying on manual inspection at the end of the production process. This research paper focuses on the utilization of transfer learning, an intelligent machine-learning technique, to improve the accuracy and efficiency of product quality inspection in production lines. The proposed approach utilizes transfer learning to adapt a pre-trained model from a related domain to the target domain, enabling accurate product quality prediction with limited data. The reference architecture provides a framework for implementing the proposed approach in a manufacturing environment, enabling real-time monitoring and decision-making based on product quality predictions. The proposed approach can improve the accuracy of faulty product detection by up to 11% compared to traditional techniques, as demonstrated by evaluations on a real-world production dataset.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130676927","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00041
S. Yao, B. R. Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Hamed Tabkhi
In recent years, smart video surveillance (SVS) systems have become essential in maintaining public safety and security, particularly in smart city environments. We propose an SVS system that uses advanced technologies such as artificial intelligence and computer vision to ensure the timely detection of anomalous behaviors and suspicious objects. The system’s performance is demonstrated through a smartphone application and real-world scenario videos, highlighting its effectiveness in enhancing citizen security with low latency. This paper represents a demonstration of such a system for implementing community-in-the-loop smart video surveillance systems and emphasizes their practicality in improving public safety in various settings. The study adds to the growing research on deploying smart video surveillance systems and underscores the importance of engaging local communities in these projects.
{"title":"Real-World Community-in-the-Loop Smart Video Surveillance System","authors":"S. Yao, B. R. Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Hamed Tabkhi","doi":"10.1109/SMARTCOMP58114.2023.00041","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00041","url":null,"abstract":"In recent years, smart video surveillance (SVS) systems have become essential in maintaining public safety and security, particularly in smart city environments. We propose an SVS system that uses advanced technologies such as artificial intelligence and computer vision to ensure the timely detection of anomalous behaviors and suspicious objects. The system’s performance is demonstrated through a smartphone application and real-world scenario videos, highlighting its effectiveness in enhancing citizen security with low latency. This paper represents a demonstration of such a system for implementing community-in-the-loop smart video surveillance systems and emphasizes their practicality in improving public safety in various settings. The study adds to the growing research on deploying smart video surveillance systems and underscores the importance of engaging local communities in these projects.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126823115","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00024
Fabrizio De Vita, Rawan M. A. Nawaiseh, Dario Bruneo, Valeria Tomaselli, Marco Lattuada, M. Falchetto
Deliver intelligence into low-cost hardware e.g., Microcontroller Units (MCUs) for the realization of low-power tailored applications nowadays is an emerging research area. However, the training of deep learning models on embedded systems is still challenging mainly due to their low amount of memory, available energy, and computing power which significantly limit the complexity of the tasks that can be executed, thus making impossible use of traditional training algorithms such as backpropagation (BP). During these years techniques such as weights compression and quantization have emerged as solutions, but they only address the inference phase. Forward-Forward (FF) is a novel training algorithm that has been recently proposed as a possible alternative to BP when the available resources are limited. This is achieved by training the layers of a neural network separately, thus reducing the required energy and memory. In this paper, we propose µ-FF, a variation of the original FF which tackles the training process with a multivariate Ridge regression approach and allows to find closed-form solution by using the Mean Squared Error (MSE) as loss function. Such an approach does not use BP and does not need to compute gradients, thus saving memory and computing resources to enable the on-device training directly on MCUs of the STM32 family. Experimental results conducted on the Fashion-MNIST dataset demonstrate the effectiveness of the proposed approach in terms of memory and accuracy.
{"title":"µ-FF: On-Device Forward-Forward Training Algorithm for Microcontrollers","authors":"Fabrizio De Vita, Rawan M. A. Nawaiseh, Dario Bruneo, Valeria Tomaselli, Marco Lattuada, M. Falchetto","doi":"10.1109/SMARTCOMP58114.2023.00024","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00024","url":null,"abstract":"Deliver intelligence into low-cost hardware e.g., Microcontroller Units (MCUs) for the realization of low-power tailored applications nowadays is an emerging research area. However, the training of deep learning models on embedded systems is still challenging mainly due to their low amount of memory, available energy, and computing power which significantly limit the complexity of the tasks that can be executed, thus making impossible use of traditional training algorithms such as backpropagation (BP). During these years techniques such as weights compression and quantization have emerged as solutions, but they only address the inference phase. Forward-Forward (FF) is a novel training algorithm that has been recently proposed as a possible alternative to BP when the available resources are limited. This is achieved by training the layers of a neural network separately, thus reducing the required energy and memory. In this paper, we propose µ-FF, a variation of the original FF which tackles the training process with a multivariate Ridge regression approach and allows to find closed-form solution by using the Mean Squared Error (MSE) as loss function. Such an approach does not use BP and does not need to compute gradients, thus saving memory and computing resources to enable the on-device training directly on MCUs of the STM32 family. Experimental results conducted on the Fashion-MNIST dataset demonstrate the effectiveness of the proposed approach in terms of memory and accuracy.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122877627","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00079
Ahmed Alsalem, Mohamed Zohdy
The Vertical Take-Off and Landing (VTOL) UAV has attracted great attention due to the long flight time and convenient take-off and landing. VTOLs are utilized in various military and civilian applications. The malfunctioning in the VTOL’s thrust due to the motor fault will result in wear and tear and significant loss to the property and human. The vibration-based signals provided by the high-rate inertial measurement unit are widely used to monitor the structural health of UAVs. In this study, a 3-axis MEMS capacitive accelerometer is proposed for low-level vibration detection of the VTOL UAV rotor. We designed and analyzed a highly sensitive three-axis capacitive accelerometer by using Finite Element Modeling (FEM). The FEM results were validated with the analytical results. The analysis results revealed that the designed accelerometer has a resonant frequency of 8515.8Hz and a sensitivity of 3.27nm/g. Additionally, the miniature-size accelerometer has a minimal impact of accelerometer weight on the net weight of a VTOL UAV. Moreover, the design accelerometer is mechanically safe, and its high sensitivity enables it to detect very low amplitude or vibration produced within VTOL UAVs.
{"title":"Sensitivity Analysis of MEMS Accelerometer for the Vibration Measurement of VTOL UAV","authors":"Ahmed Alsalem, Mohamed Zohdy","doi":"10.1109/SMARTCOMP58114.2023.00079","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00079","url":null,"abstract":"The Vertical Take-Off and Landing (VTOL) UAV has attracted great attention due to the long flight time and convenient take-off and landing. VTOLs are utilized in various military and civilian applications. The malfunctioning in the VTOL’s thrust due to the motor fault will result in wear and tear and significant loss to the property and human. The vibration-based signals provided by the high-rate inertial measurement unit are widely used to monitor the structural health of UAVs. In this study, a 3-axis MEMS capacitive accelerometer is proposed for low-level vibration detection of the VTOL UAV rotor. We designed and analyzed a highly sensitive three-axis capacitive accelerometer by using Finite Element Modeling (FEM). The FEM results were validated with the analytical results. The analysis results revealed that the designed accelerometer has a resonant frequency of 8515.8Hz and a sensitivity of 3.27nm/g. Additionally, the miniature-size accelerometer has a minimal impact of accelerometer weight on the net weight of a VTOL UAV. Moreover, the design accelerometer is mechanically safe, and its high sensitivity enables it to detect very low amplitude or vibration produced within VTOL UAVs.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659511","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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