Special Issue "Machine Learning for Signal Analysis"
A special issue of Signals (ISSN 2624-6120).
Deadline for manuscript submissions: 31 January 2024 | Viewed by 462
Interests: machine learning; deep learning; affective computing; facial expression; microexpression
Interests: data science; information technology; critical infrastructures; creative technologies
Special Issues, Collections and Topics in MDPI journals
Machine learning, in combination with signal processing, provides powerful solutions to many real-world technical and scientific challenges. Increasingly, the boundaries between the two have been blurred, such that machine learning methods are used to solve problems that were once solved using traditional signal processing methods, and signal processing methods are often used to develop or enhance new machine learning methods. This Special Issue will present the most recent and exciting advances in machine learning for signal processing. Prospective authors are invited to submit papers on relevant algorithms and applications, including, but not limited to, the following:
- Neural networks and deep learning;
- Machine learning for big data;
- Speech and audio processing applications;
- Image and video processing applications;
- Biomedical applications and neural engineering;
- Bioinformatics applications;
- Signal processing and machine learning for sensor networks;
- Continuous learning for signal analysis;
- Graphical and kernel models;
- Source separation and independent component analysis;
- Signal detection and pattern recognition as well as classification;
- Active and reinforcement learning;
- Multimodal learning for signal analysis;
- Brain–computer interface.
Prof. Dr. Xiaohua Huang
Dr. William Hurst
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
- neural network
- signal analysis
- deep learning
- machine learning
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Humanoid Control based Brain Computer Interface – A Review
Authors: Sravanth Kumar R 1, Mithileysh Sathiyanarayanan 2, Mukesh Prasad 1
Affiliation: 1. School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia 2. MIT Square, London, UK
Abstract: BCI is an interface which is used to interact by utilizing the high-level brain signals and manage formerly controlling the external sources which is sometimes done without involvement of the nervous system. These brain signals are recorded using EEG (Electroencephalography signals) on which the BCI are dependent on. These signals follow the traditional transition algorithms for generating the tasks to perform. The accuracy of these algorithms has been improved using multi sensor data fusion. This paper reviews multitudinous applications which includes commanding, navigation, extorting the objects, etc. These applications use multiple sensor fusion and ML to authorize robot in performing a desired task.