Data Analysis and Signal Processing

Dear Colleagues,

Machine learning applications have become central to solving complex tasks in recent years, driven by advances in rapid prototyping and, in many cases, reduced reliance on manual feature engineering. However, in many domains—particularly those involving high-dimensional or noisy signals—carefully designed pre-processing remains essential to maximize performance. In this context, this Special Issue, “Data Analysis and Signal Processing”, aims to highlight innovative mathematical models and signal transforms that enhance the performance of machine learning algorithms by delivering cleaner and more informative input data.

Representative examples of target signals include

• Time-series data;
• Images;
• Audio signals;
• Video;
• Multimodal sensor data.

We especially welcome submissions that

• Introduce novel signal modeling or transformation techniques;
• Demonstrate measurable improvements in ML performance using rigorous, quantitative evaluations (e.g., benchmarks on public datasets, ablation studies, statistical significance tests);
• Address current challenges such as robustness to noise, domain adaptation, computational efficiency, and integration of physics-informed priors.

Submitted manuscripts must clearly describe the derivation of the mathematical model or transformation to ensure reproducibility and comprehension. Authors are expected to make all materials necessary for reproducing their results—such as code, data (where possible), and parameter settings—publicly available. Exceptions will be considered only in  cases involving data privacy, security, or licensing constraints, in which case detailed reproduction instructions should still be provided. By combining rigorous signal processing with cutting-edge machine learning, we aim to inspire contributions that advance the state of the art in robust, efficient, and interpretable systems.

Dr. Mario González-Lee
Prof. Dr. Luís Javier Morales- Mendoza
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. Mathematics 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 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.

• machine learning
• signal processing
• time-series modeling
• dataset pre-processing
• feature engineering
• multimodal sensor data
• noise robustness
• mathematical models
• domain adaptation