Electronics

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Special Issue Editor

Prof. Dr. Heidar Malki

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Guest Editor

Department of Engineering Technology, University of Houston, Houston, TX 77204, USA
Interests: applications of neural networks; design of fuzzy logic controllers; smart grid and cybersecurity of grids; prognoses of health of lit batteries

Special Issue Information

Dear Colleagues,

The resurgence of multilayer neural network training algorithms in 1980s provided new tools to solve many complex and nonlinear systems. The resurgence of deep learning methods in recent years has allowed scientists to analyze and understand big data in all fields. Deep learning networks allows researchers and scientists to capture details and the complex behavior of physical systems. To this end, in this issue we invite scientists to submit their latest research on the applications of deep learning on all engineering fields including but not limited to image processing, recognition, classification, forecasting, and data analytics.

Prof. Dr. Heidar Malki
Guest Editor

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

Neural networks
deep learning
classification
recognitions
data analytics

Published Papers (2 papers)

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Research

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17 pages, 4444 KiB

Open AccessArticle

Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

by Nuha A. S. Alwan and Zahir M. Hussain

Electronics 2021, 10(11), 1245; https://doi.org/10.3390/electronics10111245 - 24 May 2021

Cited by 8 | Viewed by 2890

Abstract

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control. Full article

(This article belongs to the Special Issue Emerging Applications of Deep Learning in Industry)

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Review

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16 pages, 901 KiB

Open AccessReview

Attention Mechanisms in CNN-Based Single Image Super-Resolution: A Brief Review and a New Perspective

by Hongyu Zhu, Chao Xie, Yeqi Fei and Huanjie Tao

Electronics 2021, 10(10), 1187; https://doi.org/10.3390/electronics10101187 - 15 May 2021

Cited by 32 | Viewed by 4876

Abstract

With the advance of deep learning, the performance of single image super-resolution (SR) has been notably improved by convolution neural network (CNN)-based methods. However, the increasing depth of CNNs makes them more difficult to train, which hinders the SR networks from achieving greater success. To overcome this, a wide range of related mechanisms has been introduced into the SR networks recently, with the aim of helping them converge more quickly and perform better. This has resulted in many research papers that incorporated a variety of attention mechanisms into the above SR baseline from different perspectives. Thus, this survey focuses on this topic and provides a review of these recently published works by grouping them into three major categories: channel attention, spatial attention, and non-local attention. For each of the groups in the taxonomy, the basic concepts are first explained, and then we delve deep into the detailed insights and contributions. Finally, we conclude this review by highlighting the bottlenecks of the current SR attention mechanisms, and propose a new perspective that can be viewed as a potential way to make a breakthrough. Full article

(This article belongs to the Special Issue Emerging Applications of Deep Learning in Industry)

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