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Electronics
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Intelligent Embedded Systems: Latest Advances and Applications
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Intelligent Embedded Systems: Latest Advances and Applications

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 2389

Editors

Prof. Dr. Deok-Hwan Kim

E-Mail Website
Guest Editor
Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: embedded and real-time systems; cyber-physical systems (CPS) and artificial intelligence (AI); edge computing and internet of things (IoT); embedded software for robots and vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Shan Ullah

E-Mail Website
Guest Editor
Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: deep learning; computer vision; federated learning; time series classification; biosignal processing; speech processing; edge computing using embedded systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent embedded systems have become a necessity in almost every aspect of the real world. Advanced embedded systems utilizing Artificial Intelligence offer wide range of flexibility and convenience. Such applications include mission-critical and latency-sensitive embedded systems. The systems are implemented locally to complete various sets of related tasks.

Intelligent embedded systems have wide applicability, including in microcontrollers, consumer electronics, and medical devices, and in areas ranging from edge-level controllers to cloud-level control systems. Indeed, these systems, powered by Artificial Intelligence (AI), will revolutionize how we control our environment, including factories, workplaces, transportation systems, and power/water/gas grids. For example, AI-powered robots are rapidly capable of controlling multiple tasks and learning from experience while interacting with humans. Working via the human–computer interface, these system applications are broadening the scope of its use and its contributions.

This Special Issue welcomes contributions on novel ideas related to AI-powered enhanced embedded systems in various domains, such as industrial automation, manufacturing, robotics, automotive, appliance automation, healthcare, wearable systems, energy systems, smart grids, and smart cities. Potential areas of research include, but are not limited to, the following topics:

  • AI-powered advanced embedded systems and applications.;
  • Modelling and simulation of intelligent embedded systems;
  • Sensing and perception of intelligent embedded systems;
  • Enhancing energy efficiency of intelligent embedded systems and applications;
  • Reliable and fault-tolerant real-time intelligent embedded systems for IoT devices;
  • Lessons learned from large-scale real-time control of intelligent embedded systems.

Prof. Dr. Deok-Hwan Kim
Dr. Shan Ullah
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 250 words) can be sent to the Editorial Office for assessment.

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

  • intelligent embedded systems and AI applications
  • on-device AI
  • phyiscal AI
  • deep learning
  • artificial intelligence
  • internet of things
  • distributed systems
  • real-time operating systems
  • cyber–physical systems
  • real-time embedded systems
  • digital signal processing
  • edge computing
  • fault-tolerant applications
  • human–computer interface applications
  • smart devices for expert systems

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Published Papers (2 papers)

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21 pages, 964 KB  
Article
HySV: A Hymba-Inspired Hybrid-Head Framework for Quality-Aware and Deployment-Aware Speaker Verification in Intelligent Embedded Systems
by Sundareswari Thiyagarajan and Deok-Hwan Kim
Electronics 2026, 15(12), 2676; https://doi.org/10.3390/electronics15122676 - 17 Jun 2026
Viewed by 204
Abstract
Speaker verification is an important biometric technology for secure and personalized human–computer interaction in intelligent embedded systems. However, deploying deep speaker verification models on edge devices remains challenging because of restricted computational resources and strict real-time latency requirements. Existing systems commonly rely on [...] Read more.
Speaker verification is an important biometric technology for secure and personalized human–computer interaction in intelligent embedded systems. However, deploying deep speaker verification models on edge devices remains challenging because of restricted computational resources and strict real-time latency requirements. Existing systems commonly rely on convolutional, time-delay, or Transformer-based encoders. Although ECAPA-TDNN-based models provide strong verification performance, their temporal modeling mainly depends on convolutional and TDNN-style operations. Transformer-based models can capture broader temporal patterns, but they often require high computational and memory costs, making them less suitable for embedded deployment. To address these limitations, this paper proposes HySV, a Hymba-inspired hybrid attention and state-space encoder for deployment-aware speaker verification. Rather than directly employing the original Hymba language model, HySV adapts its hybrid-head principle to speaker embedding extraction. Specifically, conventional ECAPA-TDNN-style encoder blocks are replaced with three stacked Hymba context blocks. Each block contains an attention branch for local speaker-discriminative cue modeling and a state-space branch for efficient temporal context summarization. In addition, a quality-aware decision support module is introduced after cosine similarity scoring to improve reliability using utterance duration, voice activity ratio, and embedding confidence. The proposed system is evaluated using both speaker verification and deployment-oriented metrics, including EER, minDCF, FLOPs, and latency. Full article
(This article belongs to the Special Issue Intelligent Embedded Systems: Latest Advances and Applications)
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24 pages, 902 KB  
Article
Differentiable Selection of Bit-Width and Numeric Format for FPGA-Efficient Deep Networks
by Kawthar Dellel, Emanuel Trabes, Aymen Zayed, Hassene Faiedh and Carlos Valderrama
Electronics 2025, 14(18), 3715; https://doi.org/10.3390/electronics14183715 - 19 Sep 2025
Viewed by 1678
Abstract
Quantization-aware training (QAT) has emerged as a key strategy for enabling efficient deep learning inference on resource-constrained platforms. Yet, most existing approaches rely on static, manually selected numeric formats—fixed-point or floating-point—and fixed bit-widths, limiting their adaptability and often requiring extensive design effort or [...] Read more.
Quantization-aware training (QAT) has emerged as a key strategy for enabling efficient deep learning inference on resource-constrained platforms. Yet, most existing approaches rely on static, manually selected numeric formats—fixed-point or floating-point—and fixed bit-widths, limiting their adaptability and often requiring extensive design effort or architecture search. In this work, we introduce a novel QAT framework that breaks this rigidity by jointly learning, during training, both the numeric representation format and the associated bit-widths in an end-to-end differentiable manner. At the core of our method lies a unified parameterization that is capable of emulating both fixed- and floating-point arithmetic, paired with a bit-aware loss function that penalizes excessive precision in a hardware-aligned fashion. We demonstrate that our approach achieves state-of-the-art trade-offs between accuracy and compression on MNIST, CIFAR-10, and CIFAR-100, reducing average bit-widths to as low as 1.4 with minimal accuracy loss. Furthermore, FPGA implementation using Xilinx FINN confirms over 5× LUT and 4× BRAM savings. This is the first QAT method to unify numeric format learning with differentiable precision control, enabling highly deployable, precision-adaptive deep neural networks. Full article
(This article belongs to the Special Issue Intelligent Embedded Systems: Latest Advances and Applications)
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