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Information Theory and Nonlinear Signal Processing

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 2975

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: fault diagnosis of metallurgical machinery; prognostics and health management; nonlinear signal processing; structural health monitoring

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Guest Editor
Department of Engineering Technology, University of Houston, Houston, TX 77204, USA
Interests: computational optimization; maritime safety; haptics/robotics/automation

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Guest Editor
Department of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: fault diagnosis and condition monitoring; nonlinear signal processing; nonlinear dynamics; prognostics and health management; NDT&E

Special Issue Information

Dear Colleagues,

With the rapid development of information theory and nonlinear signal analysis techniques, the typical algorithms such as entropy algorithms have achieved great success across the fields of mechanical, civil, aerospace, transportation, and biomedical engineering. These kinds of algorithms provide powerful tools for quantitative analysis of different types of sensor-collected signals, including vibration signals, vibro-acoustic signals, and biomedical signals. The information theory and nonlinear signal processing contribute to reflecting the irregularity and complexity of complex systems by extracting nonlinear characteristics.

This Special Issue focuses on the new trends in information theory and nonlinear signal processing-based diagnosis and monitoring research. Both theoretical and application research will be considered. Particular attention will be paid to new entropy algorithms and applications in fault/disease diagnosis including detection and prognostics, damage tracking of complex systems, health monitoring of civil structures, structural dynamics and modal analysis, and biomedical signal analysis. Approaches of interest include quantitative analysis, data-driven techniques, and entropy-involved machine/deep learning.

We welcome the submission of contributions and applications of entropy-based or data-driven approaches that cover, but are not limited to, the following topics:

  • New entropy algorithms
  • Entropy-driven fault detection, monitoring, and prognosis
  • Fault diagnosis of mechanical equipment
  • Damage tracking of complex system
  • Structural health monitoring
  • Structural dynamics and modal analysis
  • Biomedical signal processing
  • Physiological status monitoring
  • Physics-informed machine/deep learning
  • Intelligent sensing and data fusion
  • Sensor networks in identification and monitoring

Prof. Dr. Yong Lv
Prof. Dr. Weihang Zhu
Dr. Rui 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. Entropy is an international peer-reviewed open access monthly 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

  • entropy
  • fault diagnosis
  • fault detection/prognostics
  • damage tracking
  • anomaly detection
  • structural health monitoring
  • structural dynamics
  • modal analysis
  • machine/deep learning
  • biomedical signal processing

Published Papers (2 papers)

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Research

13 pages, 1624 KiB  
Article
Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis
by Zhe Chen, Xiaodong Ma, Jielin Fu and Yaan Li
Entropy 2023, 25(8), 1175; https://doi.org/10.3390/e25081175 - 7 Aug 2023
Cited by 1 | Viewed by 1080
Abstract
Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: [...] Read more.
Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper. Full article
(This article belongs to the Special Issue Information Theory and Nonlinear Signal Processing)
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26 pages, 50883 KiB  
Article
Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function
by Guibing Li, Weidong Jin, Jiaqing Miao, Ying Tan, Yingling Li, Weixuan Zhang and Liang Li
Entropy 2023, 25(3), 523; https://doi.org/10.3390/e25030523 - 17 Mar 2023
Viewed by 999
Abstract
Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a [...] Read more.
Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8–9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8–9 RSIs and the boundaries of the transition areas. Full article
(This article belongs to the Special Issue Information Theory and Nonlinear Signal Processing)
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