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Electronics
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New Advances in Embedded Software and Applications
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New Advances in Embedded Software and Applications

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 960

Special Issue Editors

Dr. Lina Sawalha

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI 49007, USA
Interests: energy-efficient computing; hardware–software codesign; embedded systems; computer architecture
Dr. Simin Masihi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI 49007, USA
Interests: intelligent health monitoring systems; sensors; physiological signal acquisition; IoT; wearable electronics; embedded systems; flexible hybrid electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electronics invites academic and industry researchers to submit their original work to this Special Issue titled “New Advances in Embedded Software and Applications”. The demand for smarter, energy-aware, and fast-processing embedded systems has significantly increased across various domains—from automotive, Internet of Things (IoT), and smart city applications to healthcare and industrial automation. In addition, secure embedded systems play a vital role in safeguarding sensitive data and ensuring the integrity of operations in connected environments. This Special Issue aims to explore the latest innovations and trends in embedded systems, software development, and their emerging applications. 

We encourage novel and groundbreaking contributions that address critical challenges and push the boundaries of what embedded systems can achieve in our increasingly connected and big data world. We welcome original research articles and survey papers including, but not limited to, the following:

  • Innovative artificial intelligence (AI) and machine learning (ML) hardware and software solutions for embedded applications;
  • Advanced data analytics and processing targeted for edge and IoT applications;
  • Hardware–software co-design and methodologies to optimize the performance and energy consumption of embedded applications;
  • Security and privacy algorithms and solutions in AI-driven embedded systems;
  • Solutions for energy-efficient and batteryless systems including energy harvesting and conversion;
  • Non-traditional and innovative embedded systems, including wearable devices and printed and flexible electronics;
  • Emerging applications including IoT, manufacturing automation, healthcare, smart cities, wearable electronics, and sensor acquisition.

Dr. Lina Sawalha
Dr. Simin Masihi
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 systems
  • energy-efficient computing
  • machine learning
  • edge computing
  • IoT
  • energy harvesting and conversion
  • embedded software
  • secure embedded systems
  • wearable electronics
  • printed and flexible electronics

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Published Papers (2 papers)

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25 pages, 6825 KiB  
Article
Embedded System for Monitoring Fuel Cell Power Supply System in Mobile Applications
by Miroslav Matejček, Mikuláš Šostronek, Eva Popardovská, Vladimír Popardovský and Marián Babjak
Electronics 2025, 14(9), 1803; https://doi.org/10.3390/electronics14091803 - 28 Apr 2025
Viewed by 76
Abstract
This study deals with a fuel-cell-based power supply system created from a fuel cell stack with a proton exchange membrane fuel cell (PEMFC) and controller monitoring system (Horizon Fuell Cell Technologies (HFCT)). In the fuel cell (FC) stack H60, the reactants are air [...] Read more.
This study deals with a fuel-cell-based power supply system created from a fuel cell stack with a proton exchange membrane fuel cell (PEMFC) and controller monitoring system (Horizon Fuell Cell Technologies (HFCT)). In the fuel cell (FC) stack H60, the reactants are air and hydrogen. Reactants are used for the generation of electricity. The reactants supply fuel cell stacks with hydrogen through the hydrogen supply valve, and redundant reactants are extruded from the region of the 20 fuel cells of the H60 stack through the purge valve, both controlled by an FC controller. The main contribution of this study is the proposal, practical design and integration of an embedded monitoring system into the function of a fuel-cell-based power supply system for monitoring its operation parameters in mobile applications (such as in UGVs—Unmanned Ground Vehicles). The next contribution is the usage of INA226 power monitors for the measurement of input/output parameters in selected parts of the fuel-cell-based power supply system for the evaluation of electrical efficiency or power loss in the system. The third contribution is the integration of Bluetooth technology for the transfer of data from a fuel-cell-based power supply system in a mobile platform to a smartphone or PC for monitoring and data processing. At the end of this study, the computed efficiency values of the fuel cell stack, controller and switching power supply outputs are analysed, and the advantages, disadvantages and practical experience are summarized. Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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21 pages, 417 KiB  
Article
Modeling and Adaptive Resource Management for Voice-Based Speaker and Emotion Identification Through Smart Badges
by Xiaowei Liu and Alex Doboli
Electronics 2025, 14(4), 781; https://doi.org/10.3390/electronics14040781 - 17 Feb 2025
Viewed by 453
Abstract
The number of new applications addressing human activities in social settings, like groups and organizations, is on the rise. Devising an effective data collection infrastructure is critical for such applications. This paper describes a computational model and the related algorithms to design a [...] Read more.
The number of new applications addressing human activities in social settings, like groups and organizations, is on the rise. Devising an effective data collection infrastructure is critical for such applications. This paper describes a computational model and the related algorithms to design a sociometric badge for efficient data collection in applications in which speaker and emotion recognition and tracking are essential. A new computational model describes the characteristics of verbal and emotional interactions in a group. To address the requirements of changing group interactions, a self-adaptation module optimizes badge resource management to minimize data loss and modeling errors. Experiments considered scenarios for slow and regular shifts in group interactions. The proposed self-adaptation method reduces data loss by 51% to 90%, modeling errors by 28% to 44%, and computing load by 38% to 52%. Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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