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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 August 2025 | Viewed by 3806

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 (6 papers)

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Research

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16 pages, 1351 KiB  
Article
A Comparative Study on Machine Learning Methods for EEG-Based Human Emotion Recognition
by Shokoufeh Davarzani, Simin Masihi, Masoud Panahi, Abdulrahman Olalekan Yusuf and Massood Atashbar
Electronics 2025, 14(14), 2744; https://doi.org/10.3390/electronics14142744 - 8 Jul 2025
Viewed by 378
Abstract
Electroencephalogram (EEG) signals provide a direct and non-invasive means of interpreting brain activity and are increasingly becoming valuable in embedded emotion-aware systems, particularly for applications in healthcare, wearable electronics, and human–machine interactions. Among various EEG-based emotion recognition techniques, deep learning methods have demonstrated [...] Read more.
Electroencephalogram (EEG) signals provide a direct and non-invasive means of interpreting brain activity and are increasingly becoming valuable in embedded emotion-aware systems, particularly for applications in healthcare, wearable electronics, and human–machine interactions. Among various EEG-based emotion recognition techniques, deep learning methods have demonstrated superior performance compared to traditional approaches. This advantage stems from their ability to extract complex features—such as spectral–spatial connectivity, temporal dynamics, and non-linear patterns—from raw EEG data, leading to a more accurate and robust representation of emotional states and better adaptation to diverse data characteristics. This study explores and compares deep and shallow neural networks for human emotion recognition from raw EEG data, with the goal of enabling real-time processing in embedded and edge-deployable systems. Deep learning models—specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—have been benchmarked against traditional approaches such as the multi-layer perceptron (MLP), support vector machine (SVM), and k-nearest neighbors (kNN) algorithms. This comparative study investigates the effectiveness of deep learning techniques in EEG-based emotion recognition by classifying emotions into four categories based on the valence–arousal plane: high arousal, positive valence (HAPV); low arousal, positive valence (LAPV); high arousal, negative valence (HANV); and low arousal, negative valence (LANV). Evaluations were conducted using the DEAP dataset. The results indicate that both the CNN and RNN-STM models have a high classification performance in EEG-based emotion recognition, with an average accuracy of 90.13% and 93.36%, respectively, significantly outperforming shallow algorithms (MLP, SVM, kNN). Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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19 pages, 9631 KiB  
Article
Res2Former: Integrating Res2Net and Transformer for a Highly Efficient Speaker Verification System
by Defu Chen, Yunlong Zhou, Xianbao Wang, Sheng Xiang, Xiaohu Liu and Yijian Sang
Electronics 2025, 14(12), 2489; https://doi.org/10.3390/electronics14122489 - 19 Jun 2025
Viewed by 485
Abstract
Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, [...] Read more.
Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, have demonstrated state-of-the-art performance in most Natural Language Processing (NLP) and Image Recognition tasks. However, previous studies indicate that standalone Transformer and CNN architectures present distinct challenges in speaker verification. Specifically, while Transformer models deliver good results, they fail to meet the requirements of low-resource scenarios and computational efficiency. On the other hand, CNNs perform well in resource-constrained environments but suffer from significantly reduced recognition accuracy. Several existing approaches, such as Conformer, combine Transformers and CNNs but still face challenges related to high resource consumption and low computational efficiency. To address these issues, we propose a novel solution that enhances the Transformer model by introducing multi-scale convolutional attention and a Global Response Normalization (GRN)-based feed-forward network, resulting in a lightweight backbone architecture called the lightweight simple transformer (LST). We further improve LST by incorporating the Res2Net structure from CNN, yielding the Res2Former model—a low-parameter, high—precision SV model. In Res2Former, we design and implement a time-frequency adaptive feature fusion(TAFF) mechanism that enables fine-grained feature propagation by fusing features at different depths at the frame level. Additionally, holistic fusion is employed for global feature propagation across the model. To enhance performance, multiple convergence methods are introduced, improving the overall efficacy of the SV system. Experimental results on the VoxCeleb1-O, VoxCeleb1-E, VoxCeleb1-H, and Cn-Celeb(E) datasets demonstrate that Res2Former achieves excellent performance, with the Large configuration attaining Equal Error Rate (EER)/Minimum Detection Cost Function (minDCF) scores of 0.81%/0.08, 0.98%/0.11, 1.81%/0.17, and 8.39%/0.46, respectively. Notably, the Base configuration of Res2Former, with only 1.73M parameters, also delivers competitive results. Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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27 pages, 869 KiB  
Article
An Automatic Code Generation Tool Using Generative Artificial Intelligence for Element Fill-in-the-Blank Problems in a Java Programming Learning Assistant System
by Zihao Zhu, Nobuo Funabiki, Mustika Mentari, Soe Thandar Aung, Wen-Chung Kao and Yi-Fang Lee
Electronics 2025, 14(11), 2261; https://doi.org/10.3390/electronics14112261 - 31 May 2025
Viewed by 823
Abstract
Presently, Java is a fundamental object-oriented programming language that can be mastered by any student in information technology or computer science. To assist both teachers and students, we developed the Java Programming Learning Assistant System (JPLAS). It offers several types of practice [...] Read more.
Presently, Java is a fundamental object-oriented programming language that can be mastered by any student in information technology or computer science. To assist both teachers and students, we developed the Java Programming Learning Assistant System (JPLAS). It offers several types of practice problems with different levels and learning goals for step-by-step self-study, where any answer is automatically marked in the system. One challenge for teachers that is addressed with JPLAS is the generation of proper exercise problems that meet learning requirements. We implemented programs for generating new problems from given source codes, as collecting and evaluating suitable codes remains time-consuming. In this paper, we present an automatic code generation tool using generative AI to solve this challenge. Prompt engineering is used to help generate an appropriate source code, and the quality is controlled by optimizing the prompt based on the outputs. For applications in JPLAS, we implement a web application system to automatically generate an element fill-in-the-blank problem (EFP) in JPLAS. For evaluation, we select the element fill-in-the-blank problem (EFP) as the target type in JPLAS and generate several instances using this tool. The results confirm the validity and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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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
Cited by 1 | Viewed by 525
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 616
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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Review

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33 pages, 2299 KiB  
Review
Edge Intelligence in Urban Landscapes: Reviewing TinyML Applications for Connected and Sustainable Smart Cities
by Athanasios Trigkas, Dimitrios Piromalis and Panagiotis Papageorgas
Electronics 2025, 14(14), 2890; https://doi.org/10.3390/electronics14142890 - 19 Jul 2025
Viewed by 266
Abstract
Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste [...] Read more.
Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article
(This article belongs to the Special Issue New Advances in Embedded Software and Applications)
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