Special Issue "Wavelet Entropy: Computation and Applications"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (30 May 2015).
Interests: entropy; complexity; information theory; information geometry; nonlinear dynamics; computational mathematics and statistics in medicine; biomedical time series analysis; cardiac signal processing
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Wavelet Entropy (WE) is a novel tool with the ability to analyze transient features of non-stationary signals. This metric combines wavelet decomposition and entropy to estimate the degree of order/disorder of a signal with a high time-frequency resolution. Initially, Shannon entropy was proposed to quantify the energy distribution in wavelet sub-bands, the metric defined in this way being applied to a wide variety of different scenarios. Special interest has shown WE’s application to physiological signals, such as electrocardiograms, electroencephalograms, intracranial pressure recordings or evoked related potentials, in which it is able to reveal clinically useful information, e.g., in the prevention of cardiac diseases or the detection of driving fatigue. A similar attention has also initiated WE’s use in forecasting faults and dangers in modern power systems and detecting machinery vibration. Moreover, several studies have also shown the superiority of WE in analyzing the variability and complexity of climate processes compared with traditional methods. Finally, it is worth noting that WE-based analysis of electromechanical noise has recently gained an increasing interest regarding remote corrosion monitoring in industrial applications.
However, Shannon entropy can present difficulties, such as wavelet aliasing and energy leakage, in the processing of some non-stationary signals. Thus, recent studies have proposed various alternatives to compute this metric. Novel indices such as Relative WE, Wavelet Singular Entropy, Wavelet Tsallis Entropy and Wavelet Sample Entropy, amongst others, can be found in the literature. Within this context, this special issue aims to provide a forum for contributions on these new ways of computing WE and the most recent advances in its application to every scenario in which useful information can be obtained.
Prof. Dr. Raúl Alcaraz Martínez
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 papers will be 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 1800 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.
- Wavelet Transform
- Wavelet Entropy and Relative Wavelet Entropy
- Wavelet Singular Entropy
- Multiscale Wavelet Entropy
- Wavelet Tsallis Entropy
- Wavelet Sample Entropy
- Physiological signal processing (ECG, EEG, ICP, ERP, etc.)
- Biomedical time series processing (HRV, RR, PP, etc.)
- Defaults in electric power systems
- Transmission line fault detection and identification
- Image processing and compression
- Hydrologic series data analysis
- Electromechanical noise