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Synergistic Intelligence: Pioneering Deep Learning Paradigms for Multi-Sensor Systems
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Synergistic Intelligence: Pioneering Deep Learning Paradigms for Multi-Sensor Systems

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

Deadline for manuscript submissions: 10 October 2025 | Viewed by 1689

Special Issue Editor

Prof. Dr. Xue-Bo Jin

E-Mail Website
Guest Editor
Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China
Interests: multisensor fusion; statistical signal processing; video/image processing; Bayesian theory; time series analysis; artificial intelligence; target tracking and dynamic analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the rapid proliferation of sensors in practical systems has led to an exponential growth in time-series, image, and video data, a phenomenon often referred to as “sensor measurement big data”. Traditional data fusion methods have proven effective in processing limited sensor data, enabling better understanding and knowledge generation. However, with the advent of “sensor measurement big data”, there is a pressing need to explore innovative approaches to modeling and analyzing such vast datasets. Deep learning methods have already showcased their immense potential in the domains of Internet of Things, robotics, and unmanned systems, leveraging their robust modeling and nonlinear processing capabilities for big data. Nevertheless, deep learning networks often face challenges when applied to sensor measurement data, which frequently exhibits noise. As a result, traditional deep learning networks often struggle with overfitting during training. Moreover, the abundance of redundant information in extensive measurement data not only increases the training cost but also compromises system performance. Hence, deep learning networks’ theory, technology, and application research in modeling and analyzing big data for multi-sensor systems remain an open and vital area of exploration. This Special Issue aims to present cutting-edge research that revolves around groundbreaking ideas and solutions for deep learning networks in multi-sensor systems. We invite you to contribute to this Special Issue by addressing the following topics, though you need not be limited by them: advancements in noise-free deep network theory; image recognition and detection in multi-sensor systems; time-series measurement signal prediction in multi-sensor systems; performance analysis of deep networks in multi-sensor systems; novel deep network structures and training methods for complex redundant data; deep networks based on multidimensional data in multi-sensor systems; performance analysis methods for deep networks based on measurement data; fusion methods of multi-sensor systems using deep learning networks; multi-source visual perception and recognition.

Applications of multi-sensor systems in sectors such as agriculture, food, digital economy, logistics, and more are also welcome. We are eagerly looking forward to your contributions, which will help shape the future of deep learning in multi-sensor systems. Together, expand our knowledge and pave the way for groundbreaking advancements in this exciting field.

Prof. Dr. Xue-Bo Jin
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. 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

  • multi-sensor systems
  • deep learning
  • sensor data

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Published Papers (3 papers)

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Research

24 pages, 5090 KiB  
Article
A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control
by Thi Trung Tin Nguyen, Faxiang Zhang, Jing Na, Le Thai Nguyen, Gengen Li and Altyib Abdallah Mahmoud Ahmed
Sensors 2025, 25(8), 2569; https://doi.org/10.3390/s25082569 - 18 Apr 2025
Viewed by 382
Abstract
Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To [...] Read more.
Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise. Full article
(This article belongs to the Special Issue Synergistic Intelligence: Pioneering Deep Learning Paradigms for Multi-Sensor Systems)
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18 pages, 5132 KiB  
Article
Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems
by Mainak Mallick, Young-Dae Shim, Hong-In Won and Seung-Kyum Choi
Sensors 2025, 25(6), 1745; https://doi.org/10.3390/s25061745 - 12 Mar 2025
Viewed by 441
Abstract
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning [...] Read more.
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario. Full article
(This article belongs to the Special Issue Synergistic Intelligence: Pioneering Deep Learning Paradigms for Multi-Sensor Systems)
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34 pages, 31550 KiB  
Article
Deep Learning-Based Recognition and Classification of Soiled Photovoltaic Modules Using HALCON Software for Solar Cleaning Robots
by Shoaib Ahmed, Haroon Rashid, Zakria Qadir, Qudratullah Tayyab, Tomonobu Senjyu and M. H. Elkholy
Sensors 2025, 25(5), 1295; https://doi.org/10.3390/s25051295 - 20 Feb 2025
Viewed by 728
Abstract
The global installation capacity of solar photovoltaic (PV) systems is exponentially increasing. However, the accumulation of soil and debris on solar panels significantly reduces their efficiency, necessitating frequent cleaning to maintain optimal energy output. This study presents a deep learning-based approach for the [...] Read more.
The global installation capacity of solar photovoltaic (PV) systems is exponentially increasing. However, the accumulation of soil and debris on solar panels significantly reduces their efficiency, necessitating frequent cleaning to maintain optimal energy output. This study presents a deep learning-based approach for the recognition and classification of soiled PV images, aimed at enhancing the capabilities of solar cleaning robots through the HALCON software framework. Using EANN and CNN architecture along with advanced image processing techniques, the proposed system achieves precise detection and classification of soiling patterns. The HALCON framework facilitates image acquisition, preprocessing, segmentation, and deployment of trained models for robotic control. The trained models demonstrate exceptional accuracy, with the EANN and CNN achieving classification precision of 99.87% and 99.91%, respectively. Experimental results highlight the system’s potential to improve automation of cleaning strategies, reduce unnecessary cleaning cycles, and enhance the overall performance of solar panels. This research underscores the transformative role of intelligent visual analysis in optimizing maintenance practices for renewable energy applications. Full article
(This article belongs to the Special Issue Synergistic Intelligence: Pioneering Deep Learning Paradigms for Multi-Sensor Systems)
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