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Electronics
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Advanced Internet of Things Solutions and Technologies
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Advanced Internet of Things Solutions and Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 415

Special Issue Editor

Dr. Yi Li

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: Internet of Things; cloud services

Special Issue Information

Dear Colleagues,

With the rapid development of wireless networks and intelligent terminals, Internet of Things-enabled technology is evolving infrastructure from conventional operations and maintenance business models to more efficient, sustainable, smart, and resilient systems. Meanwhile, artificial intelligence, edge computing, blockchain, and other advanced technologies are paving the way for the Internet of Things (IoT) ecosystem through various means, such as communication and computing protocols, services, and configurations. The IoT has the characteristics of intelligence, autonomy, and sharing. The network nodes possess a more powerful understanding, stronger environmental adaptability and self-management, and more reliable processing tasks. Currently, IoT smart applications exist in energy, home, building, water, and city environments. However, with the development of the IoT, many new challenges and opportunities have emerged. The traditional machine learning paradigm is difficult to support the implementation of IoT applications because of its poor model interpretability, beggarly model environment adaptability, and high model inference resource consumption. Simultaneously, although the traditional cloud computing architecture can meet the computing power and storage resource requirements of computationally intensive deep learning tasks, it is not suitable for IoT scenarios that are sensitive to latency, reliability, and privacy. The emergence of new technologies such as edge computing and blockchain has further increased the complexity of information security in wireless communication-enabled IoT. For this purpose, this special issue is devoted to seeking the most recent developments and research outcomes addressing the related solutions and technological aspects of the Advanced Internet of Things.

Dr. Yi Li
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

  • network architecture and system design in IoT
  • IoT and Cloud computing
  • programming models for the IoT
  • data management in IoT
  • M2M communications in IoT
  • energy efficiency of IoT
  • privacy, security, and trust for IoT
  • context-awareness for IoT
  • modeling and simulation of large-scale IoT scenarios
  • testbed, prototype, and practical systems for IoT use cases

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Published Papers (1 paper)

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Research

22 pages, 18817 KiB  
Article
Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework
by Wei Zhang, Abderrahmane Salmi, Chifu Yang and Feng Jiang
Electronics 2024, 13(16), 3184; https://doi.org/10.3390/electronics13163184 - 12 Aug 2024
Viewed by 283
Abstract
Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively [...] Read more.
Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure. Full article
(This article belongs to the Special Issue Advanced Internet of Things Solutions and Technologies)
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