Special Issue "Machine Learning for Signals of Interests (ML4SoTs)—Theories, Algorithms, Applications and Beyond"
A special issue of Signals (ISSN 2624-6120).
Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 5211
Interests: bioscience signal processing; data modeling
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Interests: artificial intelligence and machine learning algorithm design; signal processing and parameter estimation; control of permanent-magnet synchronous machine drives; condition monitoring and fault diagnosis of industry systems
Special Issue: Machine Learning for Signals of Interests (ML4SoTs)—Theories, Algorithms, Applications, and beyond.
Signals and their processing are ubiquitous in our daily life. In a broad sense, signals can refer to anything conveying information about an object of interest and exist in a variety of formats of data.
Signals are things of interest that are observed, measured, and recorded for further study and analysis for certain specific purposes and/or interests. Examples range from biological and neurophysiological signals (e.g., electroencephalography (EEG), electrocardiogram (ECG)) to industry signals (e.g., those recorded in wind power plants) and observations of weather/climate and space weather.
Recent years have witnessed the marriage of ML and signal processing, bringing a variety of new techniques for analyzing signals of interest more effectively. This Special Issue aims to provide a platform showing the advancements of signal analysis techniques aided by ML approaches, facilitating the publication and dissemination of research findings from scientists and researchers who work in the interface and frontier of ML and signal processing and analysis.
The Special Issue, ML4SoTs, encourages submissions that focus on machine learning for a wide range of signals of interest. Proposed fields of applications include but are not limited to:
- Anomaly and outlier detection from signals;
- Fault detection and diagnosis based on signals;
- Feature selection and feature extraction from signals and time series;
- Interpretable and explainable models for signals of things;
- Machine learning for signals of things;
- Modeling and analysis of signals and time series;
- Signal modeling and forecasting;
- Signal processing and system identification;
- Signal segmentation, clustering, and pattern recognition;
- Transparent, interpretable, and parsimonious complex systems and signals;
- Wavelet for signal modeling and system identification.
Dr. Hua-Liang Wei
Dr. Zhao-Hua Liu
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. Signals is an international peer-reviewed open access quarterly 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 1000 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.
- signal processing
- system identification
- data modeling
- dynamic systems and signals
- signals of things
- machine learning
- fault diagnosis
- anomaly detection