The main aim of this paper is to present a novel speedy, lightweight, and efficient two-stage TR-PCANet model for features extraction. TR-PCANet uses PCA to learn the filters of the convolutional layers. In order to generate powerful filters, we propose to augment the data used for the training. The Filter Learning stage is followed by binary Hashing and Blockwise Histogramming stages. At the end of the network, we propose normalizing the histograms using Tied Rank normalization. Moreover, as it could positively affect the identification rates, we suggest reshaping all images using CNN as a preprocessing stage. To further enhance the recognition yields, we combine TR-PCANet with TR-ICANet and DCTNet. We conduct extensive experiments on the public AWE dataset. The obtained results have proven the efficiency of the proposed network against TR-ICANet and DCTNet as well as the relevant state-of-the-art methods including deep learning-based ones.
{"title":"Unsupervised Two-Stage TR-PCANet Deep Network For Unconstrained Ear Identification","authors":"Aicha Korichi, Meriem Korichi, Maarouf Korichi, Oussama Aiadi","doi":"10.1109/EDiS57230.2022.9996536","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996536","url":null,"abstract":"The main aim of this paper is to present a novel speedy, lightweight, and efficient two-stage TR-PCANet model for features extraction. TR-PCANet uses PCA to learn the filters of the convolutional layers. In order to generate powerful filters, we propose to augment the data used for the training. The Filter Learning stage is followed by binary Hashing and Blockwise Histogramming stages. At the end of the network, we propose normalizing the histograms using Tied Rank normalization. Moreover, as it could positively affect the identification rates, we suggest reshaping all images using CNN as a preprocessing stage. To further enhance the recognition yields, we combine TR-PCANet with TR-ICANet and DCTNet. We conduct extensive experiments on the public AWE dataset. The obtained results have proven the efficiency of the proposed network against TR-ICANet and DCTNet as well as the relevant state-of-the-art methods including deep learning-based ones.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116447405","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996498
Amine Dahane, R. Benameur, Bouabdellah Kechar
The agricultural sector has several difficulties today in ensuring the safety of the food supply. However, the Internet of Things (IoT) has recently come to light as a promising remedy with several cutting-edge uses in smart farming. The study presents the design and development of a low-cost and full-featured fog-IoT/AI system. The system has been created using open-source platforms that monitor agro-field data in real-time. However, the smallholder community is hesitant to adopt technology-based solutions. The PRIMA INTEL-IRRIS project aims to make digital and smart agricultural technologies more appealing and available to these communities by advancing the idea of intelligent irrigation “in-the-box” This study explains a low-cost fog-IoT/AI system of version 1.0 fully targeted toward smallholder farmer communities (SFCs) and how it may provide the notion of intelligent irrigation “in-the-box” concept.
{"title":"An Innovative Smart and Sustainable Low-cost Irrigation System for Smallholder Farmers' Communities","authors":"Amine Dahane, R. Benameur, Bouabdellah Kechar","doi":"10.1109/EDiS57230.2022.9996498","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996498","url":null,"abstract":"The agricultural sector has several difficulties today in ensuring the safety of the food supply. However, the Internet of Things (IoT) has recently come to light as a promising remedy with several cutting-edge uses in smart farming. The study presents the design and development of a low-cost and full-featured fog-IoT/AI system. The system has been created using open-source platforms that monitor agro-field data in real-time. However, the smallholder community is hesitant to adopt technology-based solutions. The PRIMA INTEL-IRRIS project aims to make digital and smart agricultural technologies more appealing and available to these communities by advancing the idea of intelligent irrigation “in-the-box” This study explains a low-cost fog-IoT/AI system of version 1.0 fully targeted toward smallholder farmer communities (SFCs) and how it may provide the notion of intelligent irrigation “in-the-box” concept.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124591994","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996513
Randa Nachet, T. B. Stambouli
In this paper, a real-time object detection system is proposed to reduce the risk of Sudden Infant Death Syndrome (SIDS) related to unsafe sleeping positions. The main purpose is to apply the deep learning technology and train the YOLOv5 object detection algorithm to detect and recognize whether the safest sleeping positions. Experimental results show that the proposed model is able to achieve an accuracy of more than 99%, and the inference speed has reached 2.2 ms, which makes it compatible with real-time requirements. It can be integrated into baby monitoring devices, infant safety sleep detection systems, and mobile applications.
{"title":"A Real Time Object Detection System for Infant Safe Sleep Based on YOLOv5 Algorithm","authors":"Randa Nachet, T. B. Stambouli","doi":"10.1109/EDiS57230.2022.9996513","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996513","url":null,"abstract":"In this paper, a real-time object detection system is proposed to reduce the risk of Sudden Infant Death Syndrome (SIDS) related to unsafe sleeping positions. The main purpose is to apply the deep learning technology and train the YOLOv5 object detection algorithm to detect and recognize whether the safest sleeping positions. Experimental results show that the proposed model is able to achieve an accuracy of more than 99%, and the inference speed has reached 2.2 ms, which makes it compatible with real-time requirements. It can be integrated into baby monitoring devices, infant safety sleep detection systems, and mobile applications.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129177802","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996523
Madaoui Lotfi, M. Kedir-Talha
People with lower limb amputations suffer from mobility limitations that degrade their quality of life. In this paper, we propose a hardware system dedicated to human walking activity recognition for smart prostheses, which uses a support vector machine (SVM) algorithm and time domain features to perform activity classification. To achieve a flexible and efficient hardware design, the architecture is implemented on FPGA Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been done through a comparative study, the comparison has been done between floating point MATLAB results and fixed point XSG results.
下肢截肢患者的活动能力受到限制，从而降低了他们的生活质量。本文提出了一种针对智能假肢的人体行走活动识别硬件系统，该系统采用支持向量机(SVM)算法和时域特征进行活动分类。为了实现灵活高效的硬件设计，该架构采用Xilinx System Generator (XSG)作为DSP，在FPGA Nexys 4 Artix 7板上实现。通过对比研究对所提出的系统进行了性能评价，将浮点数MATLAB结果与定点XSG结果进行了比较。
{"title":"FPGA Implementation of Support Vector Machine for Gait Activity Classification","authors":"Madaoui Lotfi, M. Kedir-Talha","doi":"10.1109/EDiS57230.2022.9996523","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996523","url":null,"abstract":"People with lower limb amputations suffer from mobility limitations that degrade their quality of life. In this paper, we propose a hardware system dedicated to human walking activity recognition for smart prostheses, which uses a support vector machine (SVM) algorithm and time domain features to perform activity classification. To achieve a flexible and efficient hardware design, the architecture is implemented on FPGA Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been done through a comparative study, the comparison has been done between floating point MATLAB results and fixed point XSG results.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126007003","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996542
Meriem Zeboudj, K. Belkadi
The growth development of communication tools and storage technologies, as well as the technical simplicity of publishing information on the Internet, have enabled the production of a giant mass of Web information for which visually impaired users often do not have a guarantee of quality or reliability; They review a lot of data to sort out the results found and finally access their need. For this, mapping information needs to a relevant and accessible document is even more complex today. In this article, we propose an approach for visually impaired people to facilitate their navigating tasks while optimizing them, in which we focus on query reformulation by metaheuristics in the Web context and the accessibility of the latter.
{"title":"Designing a Web Accessibility Environment for the Visually Impaired","authors":"Meriem Zeboudj, K. Belkadi","doi":"10.1109/EDiS57230.2022.9996542","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996542","url":null,"abstract":"The growth development of communication tools and storage technologies, as well as the technical simplicity of publishing information on the Internet, have enabled the production of a giant mass of Web information for which visually impaired users often do not have a guarantee of quality or reliability; They review a lot of data to sort out the results found and finally access their need. For this, mapping information needs to a relevant and accessible document is even more complex today. In this article, we propose an approach for visually impaired people to facilitate their navigating tasks while optimizing them, in which we focus on query reformulation by metaheuristics in the Web context and the accessibility of the latter.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133808050","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996534
Ikram Senoussaoui, M. K. Benhaoua, H. Zahaf, G. Lipari
Real-time embedded systems are increasingly being built using commercial-off-the-shelf (COTS) components. Although these components generally offer high performance, they can occasionally incur significant timing delays. Computing precise bounds on timing delays due to contention is difficult without a proper support from the hardware. Rather than estimating contention safe delays, this work aims to avoid it. We consider hardware architectures where each core has a scratchpad memory and the task execution is divided into a memory phase and a computation phase (Predictable Execution Model - PREM). Tasks are allocated to cores by a partitioned scheduling scheme. Then we schedule memory phases using a non-preemptive scheduling approach, while computation phases are scheduled using preemptive single core schedulers. This paper presents a new artificial deadline based approach to avoid contention in memory phases, where tasks memory phases are assigned appropriate deadlines and scheduled by a non-preemptive scheduler (EDF). The effectiveness of the proposed method is evaluated using a set of synthetic experiments in terms of schedulability and analysis time.
{"title":"Toward memory-centric scheduling for PREM task on multicore platforms, when processor assignments are specified","authors":"Ikram Senoussaoui, M. K. Benhaoua, H. Zahaf, G. Lipari","doi":"10.1109/EDiS57230.2022.9996534","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996534","url":null,"abstract":"Real-time embedded systems are increasingly being built using commercial-off-the-shelf (COTS) components. Although these components generally offer high performance, they can occasionally incur significant timing delays. Computing precise bounds on timing delays due to contention is difficult without a proper support from the hardware. Rather than estimating contention safe delays, this work aims to avoid it. We consider hardware architectures where each core has a scratchpad memory and the task execution is divided into a memory phase and a computation phase (Predictable Execution Model - PREM). Tasks are allocated to cores by a partitioned scheduling scheme. Then we schedule memory phases using a non-preemptive scheduling approach, while computation phases are scheduled using preemptive single core schedulers. This paper presents a new artificial deadline based approach to avoid contention in memory phases, where tasks memory phases are assigned appropriate deadlines and scheduled by a non-preemptive scheduler (EDF). The effectiveness of the proposed method is evaluated using a set of synthetic experiments in terms of schedulability and analysis time.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127555860","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996520
Aurora González-Vidal, Jesús Fernández-García, A. Skarmeta
Water scarcity is a global concern that requires a more intelligent way to manage the water network in order to minimize consumption and losses. There is a current need for new automated and intelligent decision support systems that employ data analytic advances to analyze in real-time the anomalies and problems that appear in water usage. In our case, we have developed a 3 steps system using unsupervised algorithms, that has been tested on the irrigation of urban parks. Those steps consist of grouping the parks according to similarities in the irrigation by clustering the consumption time series, searching for the dates where multivariate anomalies occur in every group using the Vector Autoregressive Model, and finally, applying an ARIMA framework to each series in the area of those dates. Our methodology reduces the time that anomaly detection univariate systems require for analysing the whole univariate time series by extracting previous knowledge on a multivariate approach. This methodology, when integrated with an IoT platform, is a tool for easing the labelling of real anomalies and can help create supervised datasets for future research in the area. The approach is used in urban scenarios, however, can easily be extended to be an application for smart agriculture scenarios.
{"title":"A combination of multi and univariate anomaly detection in urban irrigation systems","authors":"Aurora González-Vidal, Jesús Fernández-García, A. Skarmeta","doi":"10.1109/EDiS57230.2022.9996520","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996520","url":null,"abstract":"Water scarcity is a global concern that requires a more intelligent way to manage the water network in order to minimize consumption and losses. There is a current need for new automated and intelligent decision support systems that employ data analytic advances to analyze in real-time the anomalies and problems that appear in water usage. In our case, we have developed a 3 steps system using unsupervised algorithms, that has been tested on the irrigation of urban parks. Those steps consist of grouping the parks according to similarities in the irrigation by clustering the consumption time series, searching for the dates where multivariate anomalies occur in every group using the Vector Autoregressive Model, and finally, applying an ARIMA framework to each series in the area of those dates. Our methodology reduces the time that anomaly detection univariate systems require for analysing the whole univariate time series by extracting previous knowledge on a multivariate approach. This methodology, when integrated with an IoT platform, is a tool for easing the labelling of real anomalies and can help create supervised datasets for future research in the area. The approach is used in urban scenarios, however, can easily be extended to be an application for smart agriculture scenarios.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131460205","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996478
F. Bessai-Mechmache, Maya N. Ghaffar, Rayan Y. Laouti
Over the past decade, deep learning has led to a cutting-edge performance in a variety of fields. However, it faces a fundamental constraint which is the treatment of uncertainty. The representation of the model's uncertainty is of significant importance in areas subject to strict safety or reliability re-quirements. Bayesian deep learning offers a new approach that showcases the degree of reliability of predictions made by neural networks. The present work tests deep learning with Bayesian thinking through a case study of image classification. It puts into practice Bayesian inference to tackle the problem of uncertainty in deep learning and shows its correlation with data quality and model accuracy. To reach this goal, we have implemented a Bayesian convolutional neural network using the variational inference algorithm, Bayes by Backprop. The proposed model was evaluated on an image classification task, with two benchmark datasets. The results' review allowed for validation of the Bayesian approach and showed that it obtains comparable results to those of a non-Bayesian convolutional neural network. Furthermore, the uncertainty of the model was estimated in terms of aleatory and epistemic uncertainty.
在过去的十年里，深度学习在许多领域都取得了前沿的成绩。然而，它面临着一个基本的限制，即不确定性的处理。在有严格的安全或可靠性要求的领域，模型不确定性的表示是非常重要的。贝叶斯深度学习提供了一种新的方法，展示了神经网络预测的可靠性程度。本文以图像分类为例，对贝叶斯思维下的深度学习进行了验证。将贝叶斯推理应用于解决深度学习中的不确定性问题，并展示了其与数据质量和模型精度的相关性。为了达到这个目标，我们使用变分推理算法Bayes by Backprop实现了一个贝叶斯卷积神经网络。在一个图像分类任务上，用两个基准数据集对该模型进行了评估。结果审查允许验证贝叶斯方法，并表明它获得与非贝叶斯卷积神经网络相当的结果。在此基础上，对模型的不确定性进行了评价。
{"title":"Bayesian Convolutional Neural Networks for Image Classification with Uncertainty Estimation","authors":"F. Bessai-Mechmache, Maya N. Ghaffar, Rayan Y. Laouti","doi":"10.1109/EDiS57230.2022.9996478","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996478","url":null,"abstract":"Over the past decade, deep learning has led to a cutting-edge performance in a variety of fields. However, it faces a fundamental constraint which is the treatment of uncertainty. The representation of the model's uncertainty is of significant importance in areas subject to strict safety or reliability re-quirements. Bayesian deep learning offers a new approach that showcases the degree of reliability of predictions made by neural networks. The present work tests deep learning with Bayesian thinking through a case study of image classification. It puts into practice Bayesian inference to tackle the problem of uncertainty in deep learning and shows its correlation with data quality and model accuracy. To reach this goal, we have implemented a Bayesian convolutional neural network using the variational inference algorithm, Bayes by Backprop. The proposed model was evaluated on an image classification task, with two benchmark datasets. The results' review allowed for validation of the Bayesian approach and showed that it obtains comparable results to those of a non-Bayesian convolutional neural network. Furthermore, the uncertainty of the model was estimated in terms of aleatory and epistemic uncertainty.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128138635","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996477
Leyla Belaiche, L. Kahloul, Manel Houimli, Selma Sahraoui, Saber Benharzallah, M. Grid, Nedjma Abidallah
Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. Parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.
{"title":"Multi-Population-based Parallelization of Ensemble of Differential Evolution Variants for Constrained Real Parameter Optimization","authors":"Leyla Belaiche, L. Kahloul, Manel Houimli, Selma Sahraoui, Saber Benharzallah, M. Grid, Nedjma Abidallah","doi":"10.1109/EDiS57230.2022.9996477","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996477","url":null,"abstract":"Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. Parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414039","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 : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996471
I. Akli, Halimatou Boukari Alidou, A. Chekir, Samy Ouazine
Robotic platforms use RFID (Radio Frequency IDentification) technology in various fields of application (industrial, service, etc.). RFID tags can stores many information about the environment such as type of obstacles and their dimensions, humans, robots types, etc. Robot motion guided with RFID, has, thus, a tags stored data based behavior This article presents the design and the implementation of hardware and software architectures of a basic mobile robot prototype moving with RFID. The actions (walk straight, turn left/right etc.) executed by the mobile robot depend on data stored on the RFID tags. The proposed hardware architecture is based on a Raspberry Pi (single-board nano computer) for sending commands to the actuators and receiving sensorial data, and the Arduino controller-based development board for the low level control. The software architecture is based on Robot Operating System (ROS) nodes for the implementation and approach execution. The proposed architecture provides a fully functional and scalable system with integrating RFID technology.
{"title":"Basic Mobile Robot Prototyping Using RFID","authors":"I. Akli, Halimatou Boukari Alidou, A. Chekir, Samy Ouazine","doi":"10.1109/EDiS57230.2022.9996471","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996471","url":null,"abstract":"Robotic platforms use RFID (Radio Frequency IDentification) technology in various fields of application (industrial, service, etc.). RFID tags can stores many information about the environment such as type of obstacles and their dimensions, humans, robots types, etc. Robot motion guided with RFID, has, thus, a tags stored data based behavior This article presents the design and the implementation of hardware and software architectures of a basic mobile robot prototype moving with RFID. The actions (walk straight, turn left/right etc.) executed by the mobile robot depend on data stored on the RFID tags. The proposed hardware architecture is based on a Raspberry Pi (single-board nano computer) for sending commands to the actuators and receiving sensorial data, and the Arduino controller-based development board for the low level control. The software architecture is based on Robot Operating System (ROS) nodes for the implementation and approach execution. The proposed architecture provides a fully functional and scalable system with integrating RFID technology.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132165160","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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