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Sensors
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Signal Processing and Machine Learning Techniques for Intelligent Sensing Applications
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Signal Processing and Machine Learning Techniques for Intelligent Sensing Applications

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 683

Special Issue Editor

Prof. Dr. Lidia Jackowska-Strumiłło

E-Mail Website
Guest Editor
Institute of Applied Computer Science, Lodz University of Technology (TUL), 90-924 Lodz, Poland
Interests: computer engineering; electrical engineering; machine learning; modelling and monitoring of industrial objects and processes; time series prediction; signal, image and video processing; text processing and analysis; applied computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

DearColleagues,

The last few decades have seen the emergence of a new era of intelligent sensors, enabling the creation of systems and devices that improve the convenience and quality of our lives and radically change the way people perceive and understand the world. Remarkable advances in machine learning and signal processing technologies now enable us to collect, analyse, and interpret information from a wide range of intelligent sensors in a variety of applications. This has a significant impact on a wide range of areas, including computer vision systems, target tracking, object recognition, medical imaging, health monitoring, control and robotics, security systems, production quality control, autonomous vehicles, intelligent transportation systems, intelligent homes, and many others.

This Special Issue calls for innovative efforts to explore new frontiers and challenges in the application of machine learning techniques and algorithms to many areas of intelligent sensing applications.

Topics of interest include, but are not limited to, the following:

  • Intelligent sensors and systems in machine vision and control;
  • Machine learning methods for health monitoring and biomedical signal processing;
  • Pattern recognition in medical diagnosis;
  • AI-based transportation systems;
  • Deep learning architectures;
  • Data-driven models;
  • Machine learning in various sensing applications;

Prof. Dr. Lidia Jackowska-Strumiłło
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

  • machine learning
  • signal processing
  • image analysis
  • intelligent sensing applications

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Published Papers (1 paper)

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Research

23 pages, 3580 KiB  
Article
Distributed Collaborative Data Processing Framework for Unmanned Platforms Based on Federated Edge Intelligence
by Siyang Liu, Nanliang Shan, Xianqiang Bao and Xinghua Xu
Sensors 2025, 25(15), 4752; https://doi.org/10.3390/s25154752 - 1 Aug 2025
Viewed by 396
Abstract
Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this [...] Read more.
Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning Techniques for Intelligent Sensing Applications)
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