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Electronics
Special Issues
Energy-Aware and Efficient Computing and Communications, Volume II
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Energy-Aware and Efficient Computing and Communications, Volume II

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2790

Special Issue Editors

Prof. Dr. Sean (Seok-Chul) Kwon

E-Mail Website
Guest Editor
Department of Electrical Engineering, California State University, Long Beach, CA 90840, USA
Interests: energy-efficient wireless communication; multiplexing/multiple access; multiple-input multiple-output (MIMO); 5th Generation New Radio (5G NR); modulation scheme; polarizations; reference signal design; channel/interference estimation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jong Kook Kim

E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea
Interests: heterogeneity in computing systems; real-time mobile computing; performance measures; resource management; evolutionary heuristics; energy-aware computing; efficient computing; shipboard computing; distributed systems; artificial neural networks; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Joongheon Kim

E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Seoul 02841, Korea
Interests: deep reinforcement learning; mobile platforms; energy-efficient computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, power and energy consumption has become an issue because of mobile devices, high-performance computing systems, and various communication systems. For example, data centers consume huge amounts of electricity in operating massive numbers of servers and networking devices. Embedded systems for vehicles and mobile systems are energy constrained and therefore need energy awareness and efficient operation of resources to minimize the power/energy consumption. There are many efforts to efficiently use energy (e.g., smart and micro grids). Additionally, many people and institutions are looking at Green IT, where using less energy to conserve our planet and reducing pollution is the objective. It has become essential to design and build systems that reduce or efficiently use power/energy.

Prof. Dr. Sean (Seok-Chul) Kwon
Prof. Dr. Jong Kook Kim
Dr. Joongheon Kim
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

  • energy-aware computing
  • energy-aware communication systems
  • power efficient circuit design
  • power efficient embedded systems
  • green IT
  • green IoT
  • energy-efficient communication
  • green communications
  • energy-aware mobile platforms
  • energy-aware machine learning and neural networks
  • artificial intelligence techniques for energy-aware computing

Published Papers (1 paper)

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Research

14 pages, 2109 KiB  
Article
Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications
by Nathaniel Tovar, Sean (Seok-Chul) Kwon and Jinseong Jeong
Electronics 2023, 12(3), 689; https://doi.org/10.3390/electronics12030689 - 30 Jan 2023
Viewed by 1700
Abstract
Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required [...] Read more.
Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network. Full article
(This article belongs to the Special Issue Energy-Aware and Efficient Computing and Communications, Volume II)
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