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A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 28898

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Editors

Dr. Lina Sawalha

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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

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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
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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

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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-anonymized 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 (10 papers)

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Research

Jump to: Review

34 pages, 1883 KB

Open AccessArticle

Enhancing Scientific Communication and Institutional Identity Through a Retrieval-Augmented Generation Digital Personal Tutor

by Stefano Di Tore, Michele Domenico Todino, Alessio Di Paolo, Lucia Campitiello, Umberto Bilotti, Riccardo Villari and Maurizio Sibilio

Electronics 2026, 15(4), 847; https://doi.org/10.3390/electronics15040847 - 17 Feb 2026

Cited by 3 | Viewed by 913

Abstract

This project presents the development of a Retrieval-Augmented Generation (RAG) system applied to the customization of a Non-Playable Character (NPC), designed as the Non-Playable Character (NPC) of the President of the IDIS Foundation Città della Scienza (City of Science). The NPC acts as both a virtual guide and institutional ambassador within the science center, providing multilingual, interactive, and accessible communication for a broad international audience. Through the integration of generative models with a curated, validated knowledge base, the RAG system enables the NPC to provide accurate, context-sensitive, and up-to-date responses to user queries. Developed by the Teaching Learning Centre for Education and Inclusive Technologies ‘Elisa Frauenfelder’ at the University of Salerno, the system supports the museum’s educational mission by enhancing science communication and fostering inclusive digital engagement. The Non-Playable Character (NPC) features realistic facial animation, movement, and voice synthesis, creating a digital twin capable of simulating human-like interaction. This initiative exemplifies an innovative application of artificial intelligence for an inclusive and equitable quality education and contributes to the development of engaging, accessible, and personalized learning environments. Full article

(This article belongs to the Special Issue New Advances in Embedded Software and Applications)

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22 pages, 1746 KB

Open AccessArticle

A BFS-Based DEVS Simulation Kernel for HDL-Compatible Simulation

by Bo Seung Kwon, Young Shin Han and Jong Sik Lee

Electronics 2026, 15(1), 48; https://doi.org/10.3390/electronics15010048 - 23 Dec 2025

Viewed by 1041

Abstract

The Discrete Event System Specification (DEVS) formalism provides a mathematical foundation for modeling hierarchical discrete-event systems. However, the Depth-First Search (DFS) scheduling used in the classical DEVS abstract simulator conflicts with the concurrency semantics of Hardware Description Language (HDL) simulators such as Verilog or VHDL. This mismatch induces timing distortions, including pipeline skew and zero-time feedback loops. To address these limitations, this study proposes a new DEVS simulation kernel that adopts Breadth-First Search (BFS) scheduling, integrating the delta-round concept. This approach employs an event-parking mechanism that separates event computation from application, structurally aligning with HDL’s Active–NBA–Reactive phases and enabling semantically simultaneous updates without introducing additional ε-time. Case studies demonstrate that the proposed BFS-based DEVS kernel eliminates timing discrepancies in pipeline and feedback-loop structures and establishes a formal foundation for semantic alignment between DEVS and HDL simulators. Full article

(This article belongs to the Special Issue New Advances in Embedded Software and Applications)

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26 pages, 595 KB

Open AccessArticle

Reactive Load Balancing for Sentient Spaces in Absence of Cloud and Fog

by Giacomo Valente, Federica Caruso, Luigi Pomante and Tania Di Mascio

Electronics 2025, 14(17), 3458; https://doi.org/10.3390/electronics14173458 - 29 Aug 2025

Cited by 1 | Viewed by 1067

Abstract

The use of commercial off-the-shelf smart devices in digital signage for sentient spaces is emerging as a promising solution within smart city environments. In such scenarios, these devices are often required to execute resource-intensive applications despite limited local computational capacity. Although cloud and fog infrastructures have been proposed to offload demanding workloads, they are not always suitable due to privacy and security concerns. As a result, executing sentient space applications directly on smart devices may exceed their processing capabilities. To address this limitation, state-of-the-art solutions have introduced load balancing techniques for smart devices. However, these approaches typically rely on centralized coordination or require extensive system profiling, making them unsuitable for sentient spaces, where device availability is intermittent and cooperative behavior must remain lightweight, adaptive, and decentralized. This paper proposes a distributed load balancing strategy tailored for sentient spaces that operate without reliance on cloud or fog infrastructures. The approach is based on reactive cooperation among neighboring devices and employs a local feasibility-check mechanism to determine when to offload computation and which neighboring devices are available to process it. The proposed solution is evaluated in a laboratory setting that emulates a real-world sentient space scenario within a commercial mall. Experimental results show the effectiveness of the proposed approach in maintaining real-time performance and mitigating local computational overload without relying on centralized infrastructure. Even under dynamic operating conditions, the system achieves a load balancing execution time of 5 ms on an ARM Cortex-A53 processor integrated in an AMD Zynq UltraScale+ platform. Full article

(This article belongs to the Special Issue New Advances in Embedded Software and Applications)

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16 pages, 1351 KB

Open AccessArticle

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

Cited by 7 | Viewed by 3509

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 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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2867

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, 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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 5270

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 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 KB

Open AccessArticle

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 1901

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 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 KB

Open AccessArticle

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 1359

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 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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29 pages, 2205 KB

Open AccessReview

A Review of Embedded Software Architectures for Multi-Sensor Wearable Devices: Sensor Fusion Techniques and Future Research Directions

by Michail Toptsis, Nikolaos Karkanis, Andreas Giannakoulas and Theodoros Kaifas

Electronics 2026, 15(2), 295; https://doi.org/10.3390/electronics15020295 - 9 Jan 2026

Cited by 4 | Viewed by 2710

Abstract

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems. Full article

(This article belongs to the Special Issue New Advances in Embedded Software and Applications)

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33 pages, 2299 KB

Open AccessReview

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

Cited by 19 | Viewed by 6943

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 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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