Modern Circuits and Systems Technologies (MOCAST) on Machine Learning Applications in Communications and Electronics

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 14159

Special Issue Editors


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Guest Editor
Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
ELEDIA Research Center, ELEDIA@UniTN, University of Trento, DICAM - Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy
Interests: antenna array design, processing, and characterization; synthesis of complex electromagnetic devices through system-by-design techniques; surrogate-assisted optimization; learning-by-example; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece from July 5 to 7, 2021. MOCAST’s technical program includes a special session on Machine Learning Applications in Communications and Electronics. This Special Issue aims to publish extended versions of papers in the area of machine learning from the conference. Potential topics include but are not limited to the following:

  • Machine learning techniques for wireless communications;
  • Machine learning techniques for propagation modeling;
  • Machine learning techniques for antenna design;
  • Machine learning techniques for other EM problems;
  • Machine learning techniques for 5G networks and beyond;
  • Machine learning techniques for VLSI design;
  • Machine Learning techniques for signal processing;
  • Machine Learning techniques for leakage detection problems;
  • Machine Learning techniques for wired and wireless network;
  • ML techniques for biomedical applications and wireless monitoring;
  • Surrogate models for antenna design problems;
  • Other innovative ML techniques.

Prof. Dr. Sotirios Goudos
Prof. Dr. Panagiotis Sarigiannidis
Prof. Dr. Shaohua Wan
Dr. Marco Salucci
Guest Editors

Manuscript Submission Information

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Keywords

  • deep neural networks
  • random forests
  • support vector machines
  • extreme learning machines
  • Gaussian processes
  • artificial neural networks (ANNs)
  • ensemble learning methods
  • image analysis

Published Papers (2 papers)

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Review

18 pages, 778 KiB  
Review
The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review
by Konstantinos-Filippos Kollias, Christine K. Syriopoulou-Delli, Panagiotis Sarigiannidis and George F. Fragulis
Electronics 2021, 10(23), 2982; https://doi.org/10.3390/electronics10232982 - 30 Nov 2021
Cited by 28 | Viewed by 5555
Abstract
Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to [...] Read more.
Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented. Full article
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28 pages, 667 KiB  
Review
Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends
by Vasileios P. Rekkas, Sotirios Sotiroudis, Panagiotis Sarigiannidis, Shaohua Wan, George K. Karagiannidis and Sotirios K. Goudos
Electronics 2021, 10(22), 2786; https://doi.org/10.3390/electronics10222786 - 14 Nov 2021
Cited by 56 | Viewed by 7431
Abstract
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G [...] Read more.
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area. Full article
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