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Electronics
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Recent Advancements in Embedded Computing
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Recent Advancements in Embedded Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 5114

Special Issue Editors

Dr. Ali Ibrahim

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Lebanese International University, Beirut 14404, Lebanon
Interests: digital hardware implementations; embedded systems; sensors and sensory systems; hardware accelerators; embedded machine learning and neural networks; deep learning
Dr. Hamoud Younes

E-Mail Website
Guest Editor
Department of Computer and Communication Engineering, Lebanese International University, Beirut 146404, Lebanon
Interests: embedded systems; hardware accelerators; IP modeling; deep learning; TinyML

Special Issue Information

Dear Colleagues,

Recent technological advancements in embedded systems have enabled significant improvements in several application domains through the embedding of complex computational algorithms and methods into a unified electronic system. Machine learning (ML) provides powerful and effective methods to process raw data, providing valuable information about the observed phenomenon within complex applications such as computer vision, Internet of Things (IoT), autonomous vehicles, etc. This feature is of particular importance when complex sensory systems are involved, when ML-based approaches are adopted in daily life tasks or embedded into industrial devices enabling various automated and smart capabilities.

In this regard, this Special Issue aims to collect and discuss advances/novel methods and the latest results on embedded computing methods for smart industry, sensors and sensory systems, as well as methods and techniques related to integration in wearable/portable devices for the IoT.

Potential topics include but are not limited to the following:

  • Energy-efficient circuits and systems for embedded computing;
  • Embedded hardware implementations for machine/deep learning;
  • Embedded machine and deep learning architectures on low-power devices (e.g., microcontrollers);
  • Hardware-friendly algorithms and electronic systems;
  • Methods and techniques for efficient hardware implementations;
  • Software/hardware co-design approaches for efficient embedded systems.

Dr. Ali Ibrahim
Dr. Hamoud Younes
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. Electronics 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 2400 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

  • embedded electronic systems
  • embedded computing
  • efficient hardware implementations
  • embedded architectures
  • hardware friendly algorithms
  • embedded machine learning and neural networks

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Published Papers (3 papers)

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Research

17 pages, 4219 KiB  
Article
End-to-End Dataset Collection System for Sport Activities
by Matteo Fresta, Francesco Bellotti, Alessio Capello, Ali Dabbous, Luca Lazzaroni, Flavio Ansovini and Riccardo Berta
Electronics 2024, 13(7), 1286; https://doi.org/10.3390/electronics13071286 - 29 Mar 2024
Cited by 7 | Viewed by 1548
Abstract
Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic [...] Read more.
Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic contexts of use in four different sports have revealed the system’s ability to effectively collect machine learning-usable data, with an energy consumption compatible with the timeframe of most of the sport types. The proposed architecture relies on a key feature of the Measurify internet of things framework for the management of measurement data (i.e., .csv dataset management) and supports a workflow designed for efficient data labeling of signal timeseries. The architecture is independent of any specific sport, and a new dataset generation application can be set up in a few days, even by novice developers. With a view to concretely supporting the R&D community, our work is released open-source. Full article
(This article belongs to the Special Issue Recent Advancements in Embedded Computing)
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12 pages, 1612 KiB  
Article
Analysis of the Possibility of Making a Digital Twin for Devices Operating in Foundries
by Artur Lehrfeld, Krzysztof Jaśkowiec, Dorota Wilk-Kołodziejczyk, Marcin Małysza, Adam Bitka, Łukasz Marcjan and Mirosław Głowacki
Electronics 2024, 13(2), 349; https://doi.org/10.3390/electronics13020349 - 14 Jan 2024
Cited by 2 | Viewed by 1128
Abstract
This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history [...] Read more.
This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author’s device, created by the “Łukasiewicz” Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device. Full article
(This article belongs to the Special Issue Recent Advancements in Embedded Computing)
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13 pages, 336 KiB  
Article
A Reduced Hardware SNG for Stochastic Computing
by Carlos López-Magaña, Jorge Rivera, Susana Ortega-Cisneros, Federico Sandoval-Ibarra and Juan Luis Del Valle
Electronics 2023, 12(16), 3383; https://doi.org/10.3390/electronics12163383 - 8 Aug 2023
Cited by 3 | Viewed by 1696
Abstract
Stochastic Computing (SC) is an alternative way of computing with binary weighted words that can significantly reduce hardware resources. This technique relies on transforming information from a conventional binary system to the probability domain in order to perform mathematical operations based on probability [...] Read more.
Stochastic Computing (SC) is an alternative way of computing with binary weighted words that can significantly reduce hardware resources. This technique relies on transforming information from a conventional binary system to the probability domain in order to perform mathematical operations based on probability theory, where smaller amounts of binary logic elements are required. Despite the advantage of computing with reduced circuitry, SC has a well known issue; the input interface known as stochastic number generator (SNG), is a hardware consuming module, which is disadvantageous for small digital circuits or circuits with several input data. Hence, in this work, efforts are dedicated to improving a classic weighted binary SNG (WBSNG). For this, one of the internal modules (weight generator) of the SNG was redesigned by detecting a pattern in the involved signals that helped to pose the problem in a different way, yielding equivalent results. This greatly reduced the number of logical elements used in its implementation. This pattern is interpreted with Boolean equations and transferred to a digital circuit that achieves the same behavior of a WBSNG but with less resources. Full article
(This article belongs to the Special Issue Recent Advancements in Embedded Computing)
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