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Applied Sciences
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Intelligent Metaverse Platform and XR Cloud Computing
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Intelligent Metaverse Platform and XR Cloud Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 5154

Special Issue Editors

Dr. Jinsul Kim

E-Mail Website
Guest Editor
Department of Intelligent Electronic and Computer Engineering, Chonnam National University, Room 802, Building 7, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
Interests: computer network; 5G network; metaverse; MEC; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tai-Won Um

E-Mail Website
Guest Editor
Graduate School of Data Science, Chonnam National University, Gwangju 61186, Korea
Interests: IoT; digital twin; cyber-physical system; trust information management; deep reinforcement learning
Prof. Dr. Gyu Myoung Lee

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: data processing and management; data sharing and analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on intelligent metaverse platform systems, XR cloud computing, and XR content-based research. Artificial intelligence technology and state-of-the-art network technology in the metaverse environment are now essential to provide immersion to users with the required quality of experience (QoE). In this Special Issue, we introduce recent advances in research and innovation based on artificial intelligence metaverse systems, XR cloud computing, 3D graphic technology, and metaverse networking. We aim to publish the latest and most technically sound research articles demonstrating theoretical and practical contributions to high-performance intelligent metaverse platforms, advanced network approaches, and immersive 3D graphic technology.

Topics of interest for this Special Issue include, but are not limited to:

  • VR/AR/MR new media technology and applications;
  • Simulation and tracking technology for VR/AR/MR;
  • Applied computer vision for XR content;
  • Large datasets on intelligent metaverse platforms;
  • Real-time 360-degree VR graphic-rendering technology;
  • Network design for the metaverse;
  • XR content delivery networks;
  • Segement routing for XR;
  • Cloud computing;
  • In-network computing;
  • Deep learning approaches for XR content processing;
  • Applied machine learning approaches to optimize cloud computing and networking;
  • Metaverse platform QoS/QoE analysis and evaluations;
  • Three-dimensional game engines and applications applicable to the metaverse;
  • Digital twin and cyber-physical systems;
  • Computer graphic technology on metaverse platforms;
  • Networking engines for metaverse platforms;
  • Three-dimensional graphic technology for the generation of metaverse avatars;
  • Trust information management for XR environments;
  • The application of 5G/6G network technology to the metaverse platform.

Dr. Jinsul Kim
Prof. Dr. Tai-Won Um
Prof. Dr. Gyu Myoung Lee
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. Applied Sciences 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

  • virtual reality, augmented reality, and mixed reality
  • deep learning and telecommunication XR content
  • extended reality
  • metaverse platform
  • cloud computing
  • digital twins
  • three-dimensional computer graphic avatars
  • game engine with XR content
  • segment routing
  • in-network computing
  • intelligent network design

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Published Papers (2 papers)

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15 pages, 6283 KiB  
Article
XDN-Based Network Framework Design to Communicate Interaction in Virtual Concerts with Metaverse Platforms
by Sangwon Oh, Kwangmoo Chung, Ibrahim Aliyu, Minsoo Hahn, Il-Kwon Jeong, Cho-Rong Yu, Tai-Won Um and Jinsul Kim
Appl. Sci. 2023, 13(17), 9509; https://doi.org/10.3390/app13179509 - 22 Aug 2023
Cited by 4 | Viewed by 1870
Abstract
In the trend of transforming existing systems and services into non-face-to-face models, the concert industry is also showing movements toward transitioning to virtual formats. Physical concerts in the real world require venues that can accommodate hundreds to tens of thousands of spectators, but [...] Read more.
In the trend of transforming existing systems and services into non-face-to-face models, the concert industry is also showing movements toward transitioning to virtual formats. Physical concerts in the real world require venues that can accommodate hundreds to tens of thousands of spectators, but non-face-to-face methods that can accommodate large audiences face various limitations. Moreover, to elevate the satisfaction level of virtual concert attendees to that of real-world concerts, it is important to implement interaction between performers and audiences. Modern metaverse platforms apply cutting-edge network technologies to accommodate numerous users within a single channel. Many researchers are adopting network technologies such as SDN (software-defined networking) and CDN (content delivery network) to set up a virtual concert that can accommodate large audiences. In this paper, we propose a network framework to be designed for the composition of virtual concerts. In particular, we separate a channel dedicated to interaction in order to provide an immersive experience of exchanging interactions between performers and audiences. As massive audiences transmitting interaction data to the performer in a 1:N format can lead to problems with acceptance and latency, this study introduces a concept of a channel form called ‘Zone’ and proposes an interaction data channel network framework that does not compromise immersion. The proposed framework supports tasks for effectively transmitting interaction data using network technologies for metaverse platforms such as XDN and clustering algorithms such as fuzzy c-means. We also suggest a CDN-based architecture that can ensure low latency for performers to transmit interaction data to the audience. Full article
(This article belongs to the Special Issue Intelligent Metaverse Platform and XR Cloud Computing)
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17 pages, 4093 KiB  
Article
Fine-Grained Image Recognition by Means of Integrating Transformer Encoder Blocks in a Robust Single-Stage Object Detector
by Usman Ali, Seungmin Oh, Tai-Won Um, Minsoo Hann and Jinsul Kim
Appl. Sci. 2023, 13(13), 7589; https://doi.org/10.3390/app13137589 - 27 Jun 2023
Cited by 1 | Viewed by 2449
Abstract
Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class variance. Current methods address the issue through first locating [...] Read more.
Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class variance. Current methods address the issue through first locating selective regions with region proposal networks (RPNs), object localization, or part localization, followed by implementing a CNN network or SVM classifier to those selective regions. This approach, however, makes the process simple via implementing a single-stage end-to-end feature encoded with a localization method, which leads to improved feature representations of individual tokens/regions through integrating the transformer encoder blocks into the Yolov5 backbone structure. These transformer encoder blocks, with their self-attention mechanism, effectively capture global dependencies and enable the model to learn relationships between distant regions. This improves the model’s ability to understand context and capture long-range spatial relationships in an image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative feature maps from transformer encoder blocks. We established the potential of the single-stage detector for the fine-grained image recognition task, achieving state-of-the-art 93.4% accuracy, as well as outperforming existing one-stage recognition models. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances. Full article
(This article belongs to the Special Issue Intelligent Metaverse Platform and XR Cloud Computing)
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