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Electronics
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Real-Time Control of Embedded Systems, 2nd Edition
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Real-Time Control of Embedded Systems, 2nd Edition

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

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 2243

Special Issue 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 Issue Information

Dear Colleagues,

The real-time control of embedded systems has become a necessity in almost every aspect of the world. A higher level of control of embedded systems gives users a wide range of flexibility. Many such systems are mission-critical and latency-sensitive embedded systems. Embedded systems’ controllers are implemented locally to complete various sets of related tasks. Embedded controllers have a wide variety of uses, from microcontrollers to consumer electronics, weapons to medical devices, and edge-level controllers to cloud-level control systems. Real-time control of embedded systems, powered by Artificial Intelligence (AI), will revolutionise how we control environments including factories, workplaces, transportation systems, and power/water/gas grids. For example, AI-powered robots are rapidly becoming capable of controlling multiple tasks and learning from experience while interacting with humans.  

This Special Issue welcomes contributions on novel ideas related to embedded systems in various domains such as Industrial Automation, Manufacturing, Robotics, Automotive applications, Appliance Automation, Healthcare, Wearable Systems, Energy Systems, Smart Grids, and Smart Cities. This includes, but is not limited to, the following topics:

  • AI-powered real-time control for embedded systems;
  • The modelling and simulation of real-time control for embedded systems;
  • The sensing and perception of real-time controllers for embedded systems;
  • Enhancing the energy efficiency of real-time control for embedded systems;
  • Reliable and fault-tolerant real-time controllers for IoT devices;
  •  Lessons learned from large-scale real-time control of 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 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
  • parallel computing
  • 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

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Related Special Issue

Published Papers (1 paper)

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Research

20 pages, 816 KiB  
Article
Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks
by Han Wang and Deok-Hwan Kim
Electronics 2024, 13(21), 4208; https://doi.org/10.3390/electronics13214208 - 27 Oct 2024
Viewed by 1632
Abstract
In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip [...] Read more.
In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition. Full article
(This article belongs to the Special Issue Real-Time Control of Embedded Systems, 2nd Edition)
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