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Disruptive Technologies and Wireless Sensor Network Communication Algorithms (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 789

Special Issue Editors


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Guest Editor
Department of Computer Science, Namseoul University, Cheonan 31020, Republic of Korea
Interests: mobile computing; 5G; wireless sensor networks; embedded systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand
Interests: computer network and system; wireless sensor networks; embedded technology; IoT application
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Smart ICT Convergence Engineering, Konkuk University, Seoul 05029, Republic of Korea
Interests: multimedia information systems; computer vision; deep learning; digital twin; metaverse platform
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancement of 5G, embedded, and sensor technologies has resulted in the rapid development of many services and applications. These evolutions will enable development and innovation in a variety of fields, including ultra-high-definition multimedia streaming and mixed reality, massive machine-type communication, and ultra-reliable and low-latency communication. Furthermore, the Internet of Things (IoT), automation, and smart system applications will support new business and industrial innovation.

This Special Issue aims to represent the most recent advances in disruptive and sensor technology for business and industrial applications. We welcome contributions in all areas of communication and networks, including sensors, embedded systems, multimedia (AR, VR, and MR), IoT, IoM, and smart and automation systems such as automation systems, signal processing algorithms, and new or disruptive applications. Topics may include, but are not limited to:

  • 5G and next-generation networks;
  • Mixed multimedia content and applications;
  • Signal processing algorithms;
  • Deep learning;
  • Wireless sensor and networks;
  • Disruption technology and algorithms;
  • Industrial 4.0 and automation systems;
  • Resilience and reliability of the systems;
  • Smart grids and smart systems.

Dr. Cheong Ghil Kim
Dr. Gyanendra Prasad Joshi
Dr. Chayapol Kamyod
Dr. Kyoungro Yoon
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. Sensors 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 2600 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

  • 5G
  • sensors
  • mixed multimedia
  • algorithms
  • wireless sensor
  • networks
  • deep learning
  • Industry 4.0
  • automation
  • disruption
  • innovation
  • reliability

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Published Papers (1 paper)

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Research

17 pages, 17462 KiB  
Article
Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model
by Yunho Han, Jiyoung Kim, Jinyoung Lee, Jae-Ho Nah, Yo-Sung Ho and Woo-Chan Park
Sensors 2024, 24(12), 3746; https://doi.org/10.3390/s24123746 - 9 Jun 2024
Viewed by 301
Abstract
In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, [...] Read more.
In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters. Full article
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