Selected Papers from 2022 IET International Conference on Engineering Technologies and Applications

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

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 4852

Special Issue Editors


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Guest Editor
Department of Mechatronics Engineering, National Changhua University of Education, Kaohsiung 50007, Taiwan
Interests: internet of things (IoT); intelligent systems; artificial intelligence (AI); integrated circuit design; opto-electronic materials and devices
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Guest Editor
Department of Electronical Engineering, National Chung Hsing University, Taichung 402204, Taiwan
Interests: image/video processing on artificial intelligence (AI) technology; displays; 3D video processing for display technology; system-on-a-chip design for image/video processing consumer electronics; image/video signal processor design

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Co-Guest Editor
Department of Computer Science and Information Engineering, National Quemoy University, Kinmen 89250, Taiwan
Interests: fuzzy systems; neural networks; wireless networks; optimal learning algorithms; deep learning; reinforcement learning; image processing and robot system

Special Issue Information

Dear Colleagues,

The 2022 IET International Conference on Engineering Technologies and Applications (IET ICETA 2022) (http://www.iet-iceta.org/) will be held on 14–16 October in Changhua, Taiwan. IET ICETA 2022 aims to provide a platform for experts, scholars, and researchers from all over the world to convene and share novel ideas on engineering fields. Authors of accepted papers are invited to submit the extended versions (at least 60% extension for the submissions) of their original papers and contributions.

The topics of interest include but are not limited to:

  • Artificial Intelligence, machine learning, and deep learning
  • Internet of Things
  • Audio/video systems and signal processing
  • Intelligent manufacturing
  • Semiconductors and integrated circuits
  • Green energy
  • Mechatronic integration
  • Communications and networks
  • Automation and control
  • Vehicle-to-Everything and autonomous vehicles
  • Big data and clouds
  • Advanced computing and data sciences
  • RF and microwave
  • Power devices and systems
  • Security and privacy
  • Computer software and hardware
  • Consumer electronics
  • Virtual reality, augmented reality, mixed reality, and cinematic reality
  • Opto-electronic materials, devices, circuits, and systems
  • Embedded systems
  • Advanced materials and devices
  • Sensors and actuators
  • Sustainable development
  • Technology and education
  • Advanced technologies and applications

Prof. Dr. Yeong-Lin Lai
Prof. Dr. Yeong-Kang Lai
Prof. Dr. Hsuan-Ming Feng
Prof. Dr. Rung-Ching Chen
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

  • artificial intelligence, machine learning, and deep learning

  • internet of things
  • audio/video systems and signal processing
  • intelligent manufacturing
  • semiconductors and integrated circuits
  • green energy
  • mechatronic integration
  • communications and networks
  • automation and control
  • vehicle-to-everything and autonomous vehicles
  • big data and clouds
  • advanced computing and data sciences
  • RF and microwave
  • power devices and systems
  • security and privacy
  • computer software and hardware
  • consumer electronics
  • virtual reality, augmented reality, mixed reality, and cinematic reality
  • opto-electronic materials, devices, circuits, and systems
  • embedded systems
  • advanced materials and devices
  • sensors and actuators
  • sustainable development
  • technology and education
  • advanced technologies and applications

Published Papers (2 papers)

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Research

13 pages, 3064 KiB  
Communication
Visual Perception Based Intra Coding Algorithm for H.266/VVC
by Yu-Hsiang Tsai, Chen-Rung Lu, Mei-Juan Chen, Meng-Chun Hsieh, Chieh-Ming Yang and Chia-Hung Yeh
Electronics 2023, 12(9), 2079; https://doi.org/10.3390/electronics12092079 - 01 May 2023
Cited by 4 | Viewed by 1965
Abstract
The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree [...] Read more.
The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree partition structure of H.265/HEVC, the nested multi-type tree (MTT) structure of H.266/VVC provides more diverse splits through binary and ternary trees. It also includes many new coding tools, which tremendously increases the encoding complexity. This paper proposes a fast intra coding algorithm for H.266/VVC based on visual perception analysis. The algorithm applies the factor of average background luminance for just-noticeable-distortion to identify the visually distinguishable (VD) pixels within a coding unit (CU). We propose calculating the variances of the numbers of VD pixels in various MTT splits of a CU. Intra sub-partitions and matrix weighted intra prediction are turned off conditionally based on the variance of the four variances for MTT splits and a thresholding criterion. The fast horizontal/vertical splitting decisions for binary and ternary trees are proposed by utilizing random forest classifiers of machine learning techniques, which use the information of VD pixels and the quantization parameter. Experimental results show that the proposed algorithm achieves around 47.26% encoding time reduction with a Bjøntegaard Delta Bitrate (BDBR) of 1.535% on average under the All Intra configuration. Overall, this algorithm can significantly speed up H.266/VVC intra coding and outperform previous studies. Full article
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10 pages, 3964 KiB  
Article
Artificial Neural Network for Photonic Crystal Band Structure Prediction in Different Geometric Parameters and Refractive Indexes
by Fu-Li Hsiao, Hsin-Feng Lee, Su-Chao Wang, Yu-Ming Weng and Ying-Pin Tsai
Electronics 2023, 12(8), 1777; https://doi.org/10.3390/electronics12081777 - 09 Apr 2023
Cited by 4 | Viewed by 1471
Abstract
In this study, an artificial neural network that can predict the band structure of 2-D photonic crystals is developed. Three kinds of photonic crystals in a square lattice, triangular lattice, and honeycomb lattice and two kinds of materials with different refractive indices are [...] Read more.
In this study, an artificial neural network that can predict the band structure of 2-D photonic crystals is developed. Three kinds of photonic crystals in a square lattice, triangular lattice, and honeycomb lattice and two kinds of materials with different refractive indices are investigated. Using the length of the wave vectors in the reduced Brillouin zone, band number, r/a ratio, and the refractive indices as the dataset, the desired ANN is trained to predict the eigenfrequencies of the photonic modes and depict the photonic band structures with a correlation coefficient greater than 0.99. By increasing the number of neurons in the hidden layer, the correlation coefficient can be further increased over 0.999. Full article
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