IEEE
Transactions on Microwave Theory and Techniques
Special
Issue
AI
and Machine Learning Based Technologies for Microwaves
Machine
learning and AI have experienced phenomenal success in the past decade in signal
processing, image and speech recognition, robotics, autonomous systems and
more. This success is also coupled with
the expanding applications of machine learning and AI in broad areas of science
and engineering. The microwave community
is among the earliest in exploring machine learning and artificial neural
networks (ANN) for wireless and wireline electronic device, circuit
and system designs. In recent years, there
is a significant increase in the interests and activities in applying machine
learning and AI not only at device/circuit level modeling and design, but also
at system and higher-level applications. Stimulated research and applications
leads to novel methodologies of microwave oriented machine learning techniques,
such as new ANN, support vector machine and Gaussian process based approaches,
automated modeling, deep learning; in addition to an expanding scope of
microwave problems that are addressed by machine learning and AI, from electromagnetic structural modeling and design,
multi-physics modeling, microwave filter/multiplexer
design, GaN HEMT modeling, PA behavioral modeling, digital
predistortion design, oscillator design, SIW diagnosis, MEM sensor modeling,
design of high-speed VLSI packages and microsystems, wireless power transfer, MIMO
transmitter design and more. Further applications of machine learning at system
level are creating breakthrough capabilities of microwave systems, such as
electromagnetic-based image reconstruction for medical or security applications,
and dynamic spectrum allocation for next generation wireless systems.
This
special issue will bring the subject into focus, creating a forum for
researchers and engineers. The special
issue aims to stimulate in-depth overviews, thought-provoking formulations, novel
methodologies and applications. Topics include, but are not limited to:
Authors
should consult the link https://www.mtt.org/author-information-transactions/ for submission
instructions.
The
submission deadline is February 26, 2022. Expected publication August 2022.
Guest
Editor
Prof. Q.J. Zhang
Carleton
University, Ottawa, Canada
Qijun.zhang@carleton.ca