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Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 12061

Special Issue Editors

Prof. Dr. Sergio Saponara

E-Mail Website
Guest Editor
Dipartimento di Ingegneria della Informazione (DII), Università di Pisa, via G. Caruso 16, 56122 Pisa, Italy
Interests: automotive electronics; embedded HPC (high-performance computing); enabling technologies IoT (Internet of Things)
Special Issues, Collections and Topics in MDPI journals
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 academics, practitioners, and managers who operate in the field of electronic applications in various domains. The focus of this Special Issue will be on sensing systems,  IoT (Internet of Things) technologies and artificial intelligence.

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 by at least 50%) of their contributions relating to the following areas (please note that original contributions related to electronic systems and applications of the following topics will also be considered for publication):

Healthcare and Assistive Technologies: biomedical imaging; biomedical sensors, circuits and instrumentation; health monitoring; energy harvesting for biomedical applications; brain–computer interface (BCI) or brain–machine interface (BMI); human–machine interface (HMI) and augmented reality; biomimetic and bio-inspired sensors; circuits and systems; crowd-sensing and human-centric sensing

Space and avionics: sensors for remote monitoring; space and avionics sensors; redundant, secure, and rad-hard communication and computing circuits and systems; navigation and localization technologies

Autonomous and connected vehicles and smart mobility: intelligent electronics for road safety; autonomous driving electronics; autonomous driving functions; advanced driving assistance systems; driver information management; smart Li-ion batteries; intelligent transportation systems; smart mobility infrastructure; infomobility

Education, training, and entertainment: human–computer interaction; smart learning environments; technology-enhanced learning; serious games; digital learning and education; collaborative applications and systems; cultural heritage

Sensing and environment perception: 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; sensor technologies including MEMS/MOEMS

Enabling technologies: Internet of Things; artificial intelligence; machine learning; deep learning; cryptography; cyber-security and privacy; 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; edge computing; cloud 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; robotics; Industry 4.0

System engineering: system modeling and simulation; requirement engineering; testing; verification; model checking; functional safety; life-cycle management; maintenance

Prof. Dr. Sergio Saponara
Prof. Alessandro De Gloria
Prof. 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. Sensors 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 2600 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 
  • Healthcare and biomedical electronics 
  • Intelligent transportation systems 
  • Sensors and sensing systems 
  • Digital technologies and Internet of Things 
  • Cyber physical systems 
  • Industry 4.0 
  • Artificial intelligence

Published Papers (5 papers)

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Research

18 pages, 7674 KiB  
Article
VirtLAB: A Low-Cost Platform for Electronics Lab Experiments
by Massimo Ruo Roch and Maurizio Martina
Sensors 2022, 22(13), 4840; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134840 - 27 Jun 2022
Cited by 6 | Viewed by 2257
Abstract
The recent SARS-CoV2 pandemic has put a great challenge on university courses. Electronics teaching requires real laboratory experiences for students, which cannot be realized if access to physical infrastructures is prohibited. A possible solution would be to distribute to students, at home, electronics [...] Read more.
The recent SARS-CoV2 pandemic has put a great challenge on university courses. Electronics teaching requires real laboratory experiences for students, which cannot be realized if access to physical infrastructures is prohibited. A possible solution would be to distribute to students, at home, electronics equipment suitable for laboratory experiments, but no reasonable product is currently available off-the-shelf. In this paper, the design and development of a very-low-cost experimental board tailored to these needs is presented. It contains both programmable prototyping circuitry based on a microcontroller and an FPGA and a set of measurement instruments, similar to the ones found on a typical lab desk, such as a digital storage oscilloscope, multimeter, analog signal generator, logic state analyzer and digital pattern generator. A first board, suitable for analog and digital electronics experiments, has been designed and manufactured, and is described in this paper. The board has been successfully used in master’s degrees and PhD courses. Full article
(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence)
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20 pages, 4621 KiB  
Article
Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem Applications
by Riccardo Berta, Francesco Bellotti, Alessandro De Gloria and Luca Lazzaroni
Sensors 2022, 22(3), 713; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030713 - 18 Jan 2022
Cited by 7 | Viewed by 1638
Abstract
Availability of efficient development tools for data-rich IoT applications is becoming ever more important. Such tools should support cross-platform deployment and seamless and effective applicability in a variety of domains. In this view, we assessed the versatility of an edge-to-cloud system featuring Measurify, [...] Read more.
Availability of efficient development tools for data-rich IoT applications is becoming ever more important. Such tools should support cross-platform deployment and seamless and effective applicability in a variety of domains. In this view, we assessed the versatility of an edge-to-cloud system featuring Measurify, a framework for managing smart things. The framework exposes to developers a set of measurement-oriented resources that can be used in different contexts. The tool has been assessed in the development of end-to-end IoT applications in six Electronic and Information Technologies Engineering BSc theses that have highlighted the potential of such a system, both from a didactic and a professional point of view. The main design abstractions of the system (i.e., generic sensor configuration, simple language with chainable operations for processing data on the edge, seamless WiFi/GSM communication) allowed developers to be productive and focus on the application requirements and the high-level design choices needed to define the edge system (microcontroller and its sensors), avoiding the large set-up times necessary to start a solution from scratch. The experience also highlighted some usability issues that will be addressed in an upcoming release of the system. Full article
(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence)
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16 pages, 2240 KiB  
Article
Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge
by Ali Walid Daher, Ali Rizik, Marco Muselli, Hussein Chible and Daniele D. Caviglia
Sensors 2021, 21(19), 6526; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196526 - 29 Sep 2021
Cited by 7 | Viewed by 2582
Abstract
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every [...] Read more.
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task. Full article
(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence)
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16 pages, 1103 KiB  
Article
Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria
by Marco Marini, Nicola Vanello and Luca Fanucci
Sensors 2021, 21(19), 6460; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196460 - 27 Sep 2021
Cited by 6 | Viewed by 2246
Abstract
Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique [...] Read more.
Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker’s voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker’s voice features and their optimal parameters. Full article
(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence)
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11 pages, 3187 KiB  
Communication
Application of Simulated Arms with Real-Time Pressure Monitor in Casting and Splinting by Physiological Sensors
by Hsuan-Kai Kao, Yi-Chao Wu, Chi-Heng Lu, Zhong Hua, Mei-Chuan Chen and Chiu-Ching Tuan
Sensors 2021, 21(17), 5681; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175681 - 24 Aug 2021
Cited by 1 | Viewed by 1967
Abstract
In the real condition, the small sensor found it difficult to detect the position of the pressure sore because of casting displacement clinically. The large sensor will detect the incorrect pressure value due to wrinkles without close to arm. Hence, we developed a [...] Read more.
In the real condition, the small sensor found it difficult to detect the position of the pressure sore because of casting displacement clinically. The large sensor will detect the incorrect pressure value due to wrinkles without close to arm. Hence, we developed a simulated arm with physiological sensors combined with an APP and a cloud storage system to detect skin pressure in real time when applying a short arm cast or splint. The participants can apply a short arm cast or splint on the simulative arm and the pressure in the cast or splint could be immediately displaced on the mobile application. The difference of pressure values from six pressure detection points of the simulated arm between the intern and the attending physician with 20-year working experience were 22.8%, −7.3%, 25.0%, 8.6%, 38.2%, 49.6%, respectively. It showed that the difference of pressure values in two farthest points, such as radius stab and ulnar styloid, was maximal. The pressures on the skin surface of the short arm cast were within acceptable range. Doctors would obtain reliable reference data and instantly understand the tightness of the swathed cast which would enable them to adjust it at any time to avoid complications. Full article
(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensors, IoT and Artificial Intelligence)
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