Pattern Recognition and Sensor Fusion Solutions in Intelligent Sensor Systems, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1475

Special Issue Editors


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Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6725 Szeged, Hungary
Interests: intelligent sensor systems; wireless sensor networks; sensor calibration; inertial and magnetic sensors; sensor applications; human-machine interfaces; wearable sensors; sensor fusion; localization; intelligent transportation systems; vehicle detection and classification systems; robotics; mobile robots; multi-robot systems; pattern recognition; signal processing; machine learning
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Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: intelligent control; sensor fusion; robotics; kalman filtering; industrial robotics; soft computing; localization; SLAM
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Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: sensors; wearable devices; digital signal processing; motion analysis; motion pattern recognition
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Guest Editor
Software Engineering Institute, John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Interests: machine learning; deep neural networks; parallel programming
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Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: information and communication technologies; signal processing; information theory; data mining and knowledge discovery; sensors; feedback systems: biomechanical; electrical; monetary; social; economic
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Special Issue Information

Dear Colleagues,

Recent advances in technology have led to the development of various intelligent sensor systems, in which pattern recognition and sensor fusion algorithms play a crucial role in most cases. For the effective operation of these algorithms, advanced solutions are required in many areas, such as pre-processing, feature extraction, feature selection, classification, state estimation, implementation, etc.

Intelligent sensor systems can be based on signals of various sensor types. Many applications use sensors which provide 2D or 3D data, such as cameras and LiDARs, where computer-vision solutions are required. Others apply time-series analysis on signals collected from acoustic sensors, inertial sensors (IMU), magnetometers, etc. Both types or their fusion are widely used in industrial, medical, health, and entertainment applications (e.g., robotics, pose estimation, human–computer interaction, navigation, intelligent transportation systems, activity, movement analysis, etc.).

The processing can be performed on a central unit or decentralized, where the required computations are done on the embedded system of the units. Since most of the applications require real-time operation, the design of these pattern recognition and sensor fusion algorithms and their implementation on the embedded systems are challenging tasks.

The aim of this Special Issue is to invite high-quality research papers and up-to-date reviews that address challenging topics of sensory signal-based pattern recognition and sensor fusion. Topics of interest include, but are not limited to, the following:

  • Sensor calibration, pre-processing of signals;
  • Sensor technologies;
  • Signal and image analysis, feature extraction, and feature selection methods;
  • Machine learning, decision-making, and classification methods;
  • Sensor fusion methods;
  • Intelligent systems;
  • Implementation of pattern recognition and sensor fusion algorithms on embedded systems;
  • Real-time systems;
  • Novel applications of sensory signal-based pattern recognition and sensor fusion methods.

Dr. Peter Sarcevic
Dr. Akos Odry
Dr. Sara Stančin
Dr. Gábor Kertész
Prof. Dr. Sašo Tomažič
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. 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

  • pattern recognition
  • sensor fusion
  • intelligent sensor systems
  • machine learning
  • classification methods
  • pre-processing
  • signal and image analysis
  • computer vision
  • embedded systems
  • sensor applications

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Published Papers (2 papers)

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13 pages, 7339 KiB  
Article
Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data
by Seunghyun Kim, Hyunsoo Seo and Eui Chul Lee
Electronics 2024, 13(7), 1384; https://doi.org/10.3390/electronics13071384 - 5 Apr 2024
Viewed by 747
Abstract
In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes [...] Read more.
In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios. Full article
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22 pages, 9215 KiB  
Article
Identifying System Non-Linearities by Fusing Signal Bispectral Signatures
by Georgia Koukiou
Electronics 2024, 13(7), 1287; https://doi.org/10.3390/electronics13071287 - 29 Mar 2024
Viewed by 533
Abstract
Higher-order statistics investigate the phase relationships between frequency components, an aspect which cannot be treated using conventional spectral measures such as the power spectrum. Among the widely used higher-order statistics, the bispectrum ranks prominently. By delving into higher-order correlations, the bispectrum offers a [...] Read more.
Higher-order statistics investigate the phase relationships between frequency components, an aspect which cannot be treated using conventional spectral measures such as the power spectrum. Among the widely used higher-order statistics, the bispectrum ranks prominently. By delving into higher-order correlations, the bispectrum offers a means of extracting additional merits and insights from frequency coupling, enhancing our understanding of complex signal interactions. This analytical approach overcomes the limitations of traditional methods, providing a more comprehensive view of the complex relationships within the frequency domain. In this paper, the extensive use of the bispectrum in various scientific and technical areas is firstly emphasized by presenting very recent applications. The main scope of this work is to investigate the consequences of various non-linearities in the creation of phase couplings. Specifically, the quadratic, the cubic and the logarithmic non-linearities are examined. In addition, simple recommendations are given on how the underlying nonlinearity could be detected. The total approach is novel, considering the capability to distinguish from the bispectral content if two non-linearities are simultaneously present. Full article
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