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Journals
Signals
Special Issues
Recent Development of Signal Detection and Processing
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Recent Development of Signal Detection and Processing

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 30 July 2025 | Viewed by 1410

Special Issue Editors

Prof. Dr. Yang Zhang

E-Mail Website
Guest Editor
College of Instrumentation and Electrical Engineering Department, Jilin University, 938 Ximinzhu Street, Changchun, China
Interests: electromagnetic exploration; surface nuclear magnetic instruments; data processing; weak signal detection
Dr. Yuhang Guo

E-Mail Website
Guest Editor
College of Geo-Exploration Science and Technology, Jilin University, 938 Ximinzhu Street, Changchun, China
Interests: digital core; NMR; resistivity; logging interpretation; porous media
Prof. Dr. Junfeng Yuan

E-Mail Website
Guest Editor
P. I., Sr. R&D Engineer (Canada), School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Interests: deep earth exploration technology and equipment; robotics; manufacturing; electromagnetic method

Special Issue Information

Dear Colleagues,

Signal detection and processing technology plays a crucial role in multiple fields such as geophysics, communication, radar and sonar, medical diagnosis, image processing and computer vision, audio and speech processing, electronic engineering, control systems, and the Internet of Things and sensor networks. With the continuous development of technology, the application fields of these technologies will continue to expand and deepen. In this context, this Special Issue aims to foster discussions about the design, implementation, evaluation, and application of emerging signal detection and processing techniques in various fields among practitioners, researchers, and educators. This Special Issue solicits articles addressing numerous topics, including but not limited to the following:

  • Theory of signal detection and processing;
  • Design of signal detection and processing system;
  • Application of signal detection and processing method;
  • Recent developments of signal detection and processing.

Prof. Dr. Yang Zhang
Dr. Yuhang Guo
Prof. Dr. Junfeng Yuan
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • weak signal detection
  • signal processing
  • noise suppression
  • filter technology
  • low noise detection system

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Published Papers (1 paper)

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Research

35 pages, 6560 KiB  
Article
Adversarial Content–Noise Complementary Learning Model for Image Denoising and Tumor Detection in Low-Quality Medical Images
by Teresa Abuya, Richard Rimiru and George Okeyo
Signals 2025, 6(2), 17; https://doi.org/10.3390/signals6020017 - 3 Apr 2025
Viewed by 570
Abstract
Medical imaging is crucial for disease diagnosis, but noise in CT and MRI scans can obscure critical details, making accurate diagnosis challenging. Traditional denoising methods and deep learning techniques often produce overly smooth images that lack vital diagnostic information. GAN-based approaches also struggle [...] Read more.
Medical imaging is crucial for disease diagnosis, but noise in CT and MRI scans can obscure critical details, making accurate diagnosis challenging. Traditional denoising methods and deep learning techniques often produce overly smooth images that lack vital diagnostic information. GAN-based approaches also struggle to balance noise removal and content preservation. Existing research has not explored tumor detection after image denoising; instead, it has concentrated on content and noise learning. To address these challenges, this study proposes the Adversarial Content–Noise Complementary Learning (ACNCL) model, which enhances image denoising and tumor detection. Unlike conventional methods focusing solely on content or noise learning, ACNCL simultaneously learns both through dual predictors, ensuring the complementary reconstruction of high-quality images. The model integrates multiple denoising techniques (DnCNN, U-Net, DenseNet, CA-AGF, and DWT) within a GAN framework, using PatchGAN as a local discriminator to preserve fine image textures. The ACNCL separates anatomical details and noise into distinct pathways, ensuring stable noise reduction while maintaining structural integrity. Evaluated on CT and MRI datasets, ACNCL demonstrated exceptional performance compared to traditional models both qualitatively and quantitatively. It exhibited strong generalization across datasets, improving medical image clarity and enabling earlier tumor detection. These findings highlight ACNCL’s potential to enhance diagnostic accuracy and support improved clinical decision-making. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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