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Special Issue "Applications in Electronics Pervading Industry, Environment and Society—Industrial Electronics, Mechatronics and Cyber Physical Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Smart Cities and Urban Management".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 2288

Special Issue Editors

Prof. Dr. Alessandro De Gloria
E-Mail Website
Guest Editor
Department of Naval, Electrical and Electronic and Telecommunication Engineering (DITEN), University of Genoa, Genoa, Italy
Interests: electronic systems and applications; serious games; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Bellotti
E-Mail Website
Guest Editor
Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department, University of Genoa, 16145 Genoa, Italy
Interests: electric vehicles; intelligent transportation systems; edge computing; Internet of Things; cyber–physical systems; human–computer interaction; serious games
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Riccardo Berta
E-Mail Website
Guest Editor
Electrical, Electronics and Telecommunication Engineering and Naval ArchitectureDepartment, University of Genoa, 16145 Genoa, Italy
Interests: Internet of Things; electronic systems and applications; edge computing; serious games
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Conference on Applications in Electronics Pervading Industry, Environment and Society (ApplePies 2020), https://applepies.eu, provided an opportunity for reciprocal meeting and knowledge on industrial and research activities for academicians, practitioners, and managers who operate in the field of electronic applications. The specific focus of this Special Issue will be on Mechatronics, Industrial Electronics and Cyber Physical Systems.

The conference offered a venue for presenting original research works, achievements, and panels on the latest trends in electronic applications pervading Industry 4.0, the environment, and society. Authors of papers accepted at the conference are invited to submit a version extended of at least 50% of their contribution to the following areas (original contributions related to electronic systems and application in the following topic will be also considered for publication):

Secure, Clean and Efficient Energy: smart grids; electronics for PV energy production; residential microgrids; domestic and residential energy storage systems; power converters; energy scavenging; energy storage; battery management systems; internet of energy; positive energy districts (PED); zero energy buildings (ZEB)

Environment: wireless sensors networks; energy harvesting for autonomous systems; environment monitoring and control; smart sensors for environmental applications, IoT and sustainable development; smart agriculture and food systems

Smart, Green, and Integrated Transportation: driver information management; intelligent electronics for road safety; autonomous driving electronics; smart Li-ion batteries; intelligent transportation systems; serious games for transportation and mobility

Enabling Technologies: industrial Internet of Things; machine learning and deep neural networks; cryptography; cyber-physical systems; embedded systems; high performance computing; (open source) HW/SW platforms; sensors and actuators; silicon-photonics and optical communications; “makers” systems; system of systems; ubiquitous computing; wireless communications; radio frequency identification (RFID); digital signal and image processing; ultra-low-energy and low-power computation and storage; wired and power-line communications; 5G networks; cognitive systems; robotics; Industry 4.0; mechatronics

System Engineering: system modeling and simulation; requirement engineering; testing and verification; cultural heritage; serious games design and implementation; digital learning and education; collaborative applications and systems

Prof. Dr. Sergio Saponara
Prof. Dr. Alessandro De Gloria
Prof. Dr. Francesco Bellotti
Prof. Dr. Riccardo Berta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Electronic systems and applications
  • Smart, green, and integrated transportation
  • Electronics for smart grids and renewable energy sources
  • Electronics for secure, clean, and efficient energy production, storage, and management
  • Digital technologies and industrial Internet of Things
  • Cyber physical systems
  • Mechatronics and robotics
  • Industry 4.0

Related Special Issue

Published Papers (3 papers)

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Research

Article
FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries
Energies 2021, 14(21), 7064; https://doi.org/10.3390/en14217064 - 28 Oct 2021
Cited by 1 | Viewed by 634
Abstract
Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. [...] Read more.
Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%. Full article
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Article
Self-Learning Pipeline for Low-Energy Resource-Constrained Devices
Energies 2021, 14(20), 6636; https://doi.org/10.3390/en14206636 - 14 Oct 2021
Cited by 1 | Viewed by 611
Abstract
The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. [...] Read more.
The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness. Full article
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Article
An Open-Hardware and Low-Cost Maintenance Tool for Light-Electric-Vehicle Batteries
Energies 2021, 14(16), 4962; https://doi.org/10.3390/en14164962 - 13 Aug 2021
Cited by 1 | Viewed by 538
Abstract
The large increment expected in the diffusion of light-electric-vehicles will raise several issues that must be addressed to cope with this trend, including battery diagnostic and maintenance services. The battery system is the most expensive part in the majority of the e-mobility devices. [...] Read more.
The large increment expected in the diffusion of light-electric-vehicles will raise several issues that must be addressed to cope with this trend, including battery diagnostic and maintenance services. The battery system is the most expensive part in the majority of the e-mobility devices. Therefore, battery manufacturers tend to reduce the battery cost by using simple battery management systems that provide only basic safety features. Possible advanced functionalities are not implemented and the battery may lose performanceduring its use. Widely spread maintenance centers are thus required to support the mobility electrification process, but their diffusion is limited by the high cost ofprofessional battery characterization instruments. This work proposes an open-hardware low-cost battery maintenance tool architecture that can be used with common laboratory instruments. The tool is based on a relay-matrix and a battery monitor integrated circuit. It is able to completely characterize and optimize the state of a battery independently of the battery management system and also gives a figure of the individual aging of the battery cells. The work shows the architecture and the experimental validation of a 16-cells battery maintenance tool prototype. The results demonstrate that utilizing the tool brings the battery in the best possible state and identifies the degradation of the cells in terms of capacity and resistance. Full article
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