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 17767
Special Issue Editors
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals
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
Interests: Internet of Things (IoT); edge computing; machine learning; computer vision; cyber physical systems; future Internet architecture and smart-energy
Special Issues, Collections and Topics in MDPI journals
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
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