Circuit Design for Embedded Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 624

Special Issue Editors


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Guest Editor
Department of Health Science, Magna Græcia University, 88100 Catanzaro, Italy
Interests: Electronic system; sensor’s systems; biopotential signals; image classification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
BATS Laboratory, Department of Health Science, Magna Græcia University, 88100 Catanzaro, Italy
Interests: discrete and silicon micro and nanosensors based on ferroelectric and conducting polymers (ultrasonic transducers, pyroelectric sensors; electronic interfaces, field effect based sensors), as well as nanoporous materials (for an application in the fields of robotics and medicine)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

This Special Issue on “Circuit Design for Embedded Systems” seeks to provide a platform for researchers to showcase their latest work and findings in the field of PCB design for embedded systems. As the demand for mobile, wearable, and electro-medical devices grows, embedded electronic circuits are becoming increasingly compact. The integration of advanced functionalities—such as connectivity, high-resolution displays, and AI-driven capabilities—has significantly increased PCB component density, driving the need for innovative solutions to achieve smaller sizes while maintaining high performance.

Key requirements for embedded applications, such as high integration, low power consumption, and effective thermal management, are at the forefront of this Special Issue. Topics of interest include, but are not limited to, PCB size reduction techniques, performance optimization of embedded systems, integration of software/hardware systems for signal monitoring, implementation of AI algorithms on circuits, and data collection, storage, and processing for embedded applications. Contributions from interdisciplinary teams are especially encouraged, as are systematic survey papers exploring recent advancements in these areas.

We invite researchers to share novel achievements and insights in the following (but not limited to) topics of interest:

  • Embedded systems for low-, medium-, and high-frequency ultrasonic applications, as well as infrared.
  • Development of wearable devices for non-invasive parameter assessment.
  • Embedded systems for biomedical, energy, and environmental applications.
  • Energy harvesting solutions designed and implemented for embedded systems.
  • AI techniques developed and deployed in embedded system applications.

We look forward to your valuable contributions to this Special Issue, driving innovation and progress in the field of circuit design for embedded systems.

Dr. Filippo Laganà
Prof. Dr. Antonino S. Fiorillo
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-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.

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Keywords

  • electronic system
  • sensor systems
  • IoT
  • energy harvesting
  • monitoring system

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Published Papers (1 paper)

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Research

23 pages, 2941 KiB  
Article
FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation
by Filippo Laganà, Diego Pellicanò, Mariangela Arruzzo, Danilo Pratticò, Salvatore A. Pullano and Antonino S. Fiorillo
Electronics 2025, 14(11), 2268; https://doi.org/10.3390/electronics14112268 - 31 May 2025
Viewed by 257
Abstract
The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces [...] Read more.
The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mechanical model, developed in COMSOL 6.2 Multiphysics, simulates the interaction between a robotic glove and a deformable latex sphere. The latex material is described using a two-parameter Mooney–Rivlin hyperelastic formulation to capture large nonlinear deformations under realistic contact conditions. The high-fidelity simulation data are used to validate the signal acquisition chain and to train a supervised AI algorithm capable of classifying rehabilitation progress—whether improving or worsening—based on biomechanical features. An integrated electronic prototype enables seamless data flow to a cloud-based monitoring platform, supporting real-time feedback and adaptability. The classification algorithm demonstrates robust performance across different test conditions, while the electronic system confirms its applicability in rehabilitation settings. The novelty of this paper lies in the closed-loop integration of FEM-based simulation, AI-driven analysis, and embedded electronics into a unified monitoring architecture. This intelligent and non-invasive approach provides a scalable tool for tracking motor recovery and enhancing therapy effectiveness through adaptive, feedback-driven interventions. Full article
(This article belongs to the Special Issue Circuit Design for Embedded Systems)
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