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Signals
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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: 31 May 2026 | Viewed by 11609

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 250 words) can be sent to the Editorial Office for assessment.

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 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 1200 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

22 pages, 4529 KB  
Article
Active Vibration Control of a Servo-Driven Pneumatic Isolation Platform for Airborne Electromagnetic Detection Systems
by Ziqiang Zhu, Haigen Zhou, Ao Wei, Junfeng Yuan, Handong Tan, Manping Yang, Zuoxi Jiang and Marco Alfano
Signals 2026, 7(2), 30; https://doi.org/10.3390/signals7020030 - 1 Apr 2026
Viewed by 626
Abstract
Airborne electromagnetic detection systems are highly susceptible to low-frequency motion-induced noise, which significantly degrades the extraction of weak geological signals. Conventional signal processing methods alone are often insufficient to suppress mechanically induced vibration noise, resulting in signal distortion and reduced detection reliability. To [...] Read more.
Airborne electromagnetic detection systems are highly susceptible to low-frequency motion-induced noise, which significantly degrades the extraction of weak geological signals. Conventional signal processing methods alone are often insufficient to suppress mechanically induced vibration noise, resulting in signal distortion and reduced detection reliability. To address this limitation, this study proposes an active noise suppression strategy that integrates mechanical vibration isolation with advanced signal processing. A pneumatic vibration isolation platform based on a cable-driven parallel robot (CDPR) architecture is developed to achieve precise orientation correction and effective vibration isolation. The system employs kinematic modeling and a servo-controlled pneumatic cylinder driven by a proportional directional valve to enable accurate dynamic regulation. Numerical simulations conducted in the Advanced Modeling and Simulation Environment (AMESim), combined with proportional–integral–derivative (PID) control, demonstrate that piston displacement overshoot is constrained within 0.2 mm. Furthermore, targeted filtering techniques are applied to enhance signal quality. Experimental results show that the response time for continuous step input is 0.18–0.2 s, with a steady-state error below 0.3 mm, confirming robust control performance. The proposed framework provides an effective low-noise solution for airborne electromagnetic detection and can improve survey reliability in deep resource exploration. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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17 pages, 9683 KB  
Article
Combined Infinity Laplacian and Non-Local Means Models Applied to Depth Map Restoration
by Vanel Lazcano, Mabel Vega-Rojas and Felipe Calderero
Signals 2026, 7(1), 2; https://doi.org/10.3390/signals7010002 - 7 Jan 2026
Viewed by 667
Abstract
Scene depth information is a key component of any robotic mobile application. Range sensors, such as LiDAR, sonar, or radar, capture depth data of a scene. However, the data captured by these sensors frequently presents missing regions or information with a low confidence [...] Read more.
Scene depth information is a key component of any robotic mobile application. Range sensors, such as LiDAR, sonar, or radar, capture depth data of a scene. However, the data captured by these sensors frequently presents missing regions or information with a low confidence level. These missing regions in the depth data could be large areas without information, making it difficult to make decisions, for instance, for an autonomous vehicle. Recovering depth data has become a primary activity for computer vision applications. This work proposes and evaluates an interpolation model to infer dense depth maps from a Lab color space reference picture and an incomplete-depth image embedded in a completion pipeline. The complete proposal pipeline comprises convolutional layers and a convex combination of the infinity Laplacian and non-local means model. The proposed model infers dense depth maps by considering depth data and utilizing clues from a color picture of the scene, along with a metric for computing differences between two pixels. The work contributes (i) the convex combination of the two models to interpolate the data, and (ii) the proposal of a class of function suitable for balancing between different models. The obtained results show that the model outperforms similar models in the KITTI dataset and outperforms our previous implementation in the NYU_v2 dataset, dropping the MSE by 34.86%, 3.35%, and 34.42% for 4×, 8×, 16× upsampling tasks, respectively. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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17 pages, 1332 KB  
Article
A Dynamic Empirical Bayes Signal Model for Attribute Defect Detection
by Yadpirun Supharakonsakun
Signals 2025, 6(4), 71; https://doi.org/10.3390/signals6040071 - 8 Dec 2025
Viewed by 859
Abstract
This study evaluates Empirical Bayes (EB) c-charts for monitoring count-type data under precautionary (PLF) and logarithmic (LLF) loss functions. By assuming an exponential prior for the Poisson mean, the EB framework enables the construction of predictive densities for future observations. Simulation studies and [...] Read more.
This study evaluates Empirical Bayes (EB) c-charts for monitoring count-type data under precautionary (PLF) and logarithmic (LLF) loss functions. By assuming an exponential prior for the Poisson mean, the EB framework enables the construction of predictive densities for future observations. Simulation studies and a real-world dataset on missing rivets in large aircraft were used to compare the methods’ ability to detect out-of-control conditions. The results show that EB–LLF charts exhibit high sensitivity for small and moderate process shifts, and both EB approaches outperform the classical c-chart by integrating prior information to detect shifts earlier while controlling false alarms. These findings highlight the importance of loss function choice and demonstrate the effectiveness of EB charts for robust process monitoring. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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22 pages, 1040 KB  
Article
ROC Calculation for Burst Traffic Packet Detection—An Old Problem, Newly Revised
by Marco Krondorf
Signals 2025, 6(4), 57; https://doi.org/10.3390/signals6040057 - 23 Oct 2025
Viewed by 1787
Abstract
Burst traffic radio systems use short signal bursts, which are prepended with an a priori known preamble sequence. The burst receivers exploit these preamble sequences for burst start detection. The process of burst start detection is commonly known as Packet Detection (PD), which [...] Read more.
Burst traffic radio systems use short signal bursts, which are prepended with an a priori known preamble sequence. The burst receivers exploit these preamble sequences for burst start detection. The process of burst start detection is commonly known as Packet Detection (PD), which employs preamble sequence cross-correlation and threshold detection. One major figure of merit for PD performance is the so-called ROC—receiver operating characteristics. ROC describes the trade-off between the probability of missed detection vs. the probability of false alarm. This article describes how to calculate the ROC for specified preamble sequences by deriving the probability density function (PDF) of the cross-correlation metric. We address this long-standing problem in the context of LEO (low Earth orbit) satellite systems, where differentially modulated PN (pseudo-noise) sequences are used for packet detection. For this particular class of preamble signals, the standard Ricean PDF assumption no longer holds and needs to be revised accordingly within this article. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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21 pages, 3492 KB  
Article
A Fuzzy Model for Predicting the Group and Phase Velocities of Circumferential Waves Based on Subtractive Clustering
by Youssef Nahraoui, El Houcein Aassif, Samir Elouaham and Boujemaa Nassiri
Signals 2025, 6(4), 56; https://doi.org/10.3390/signals6040056 - 16 Oct 2025
Cited by 2 | Viewed by 1058
Abstract
Acoustic scattering is a highly effective tool for non-destructive control and structural analysis. In many real-world applications, understanding acoustic scattering is essential for accurately detecting and characterizing defects, assessing material properties, and evaluating structural integrity without causing damage. One of the most critical [...] Read more.
Acoustic scattering is a highly effective tool for non-destructive control and structural analysis. In many real-world applications, understanding acoustic scattering is essential for accurately detecting and characterizing defects, assessing material properties, and evaluating structural integrity without causing damage. One of the most critical aspects of characterizing targets—such as plates, cylinders, and tubes immersed in water—is the analysis of the phase and group velocities of antisymmetric circumferential waves (A1). Phase velocity helps identify and characterize wave modes, while group velocity allows for tracking energy, detecting, and locating anomalies. Together, they are essential for monitoring and diagnosing cylindrical shells. This research employs a Sugeno fuzzy inference system (SFIS) combined with a Fuzzy Subtractive Clustering (FSC) identification technique to predict the velocities of antisymmetric (A1) circumferential signals propagating around an infinitely long cylindrical shell of different b/a radius ratios, where a is the outer radius, and b is the inner radius. These circumferential waves are generated when the shell is excited perpendicularly to its axis by a plane wave. Phase and group velocities are determined by using resonance eigenmode theory, and these results are used as training and testing data for the fuzzy model. The proposed approach demonstrates high accuracy in modeling and predicting the behavior of these circumferential waves. The fuzzy model’s predictions show excellent agreement with the theoretical results, as confirmed by multiple error metrics, including the Mean Absolute Error (MAE), Standard Error (SE), and Mean Relative Error (MRE). Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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16 pages, 2361 KB  
Article
Object Part-Aware Attention-Based Matching for Robust Visual Tracking
by Janghoon Choi
Signals 2025, 6(3), 47; https://doi.org/10.3390/signals6030047 - 10 Sep 2025
Viewed by 1337
Abstract
In this paper, we propose a novel visual tracking method with a object part-aware attention-based matching (OPAM) mechanism, which leverages local–global attention to enhance visual tracking performance. Our method introduces three key components: (1) a local part-aware global self-attention mechanism that embeds rich [...] Read more.
In this paper, we propose a novel visual tracking method with a object part-aware attention-based matching (OPAM) mechanism, which leverages local–global attention to enhance visual tracking performance. Our method introduces three key components: (1) a local part-aware global self-attention mechanism that embeds rich contextual information among candidate regions, enabling the model to capture mutual dependencies and relationships effectively, (2) a local part-aware global cross-attention mechanism that injects target-specific information into candidate region features, improving the alignment and discrimination between the target and background, and (3) a global cross-attention mechanism that extracts object holistic information from the target-search feature context for further discriminability. By integrating these attention modules, our approach achieves robust feature aggregation and precise target localization. Extensive experiments on a large-scale tracking benchmark demonstrate that our method shows competitive performance metrics in both accuracy and robustness, particularly under challenging scenarios such as occlusion and appearance changes, while running at real-time speeds. Full article
(This article belongs to the Special Issue Recent Development of Signal Detection and Processing)
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35 pages, 6560 KB  
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
Cited by 1 | Viewed by 3110
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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