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Axioms
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Advances in Mathematical Methods in Signal Processing and Its Applications
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Advances in Mathematical Methods in Signal Processing and Its Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 2595

Special Issue Editor

Dr. Dobrea Dan-Marius

E-Mail Website
Guest Editor
Faculty of Electronics, Telecommunication and Information Technology, “Gheorghe Asachi” Technical University, 679048 Iași, Romania
Interests: signal processing; machine learning; intelligent systems; bio-inspired systems; Internet of Things; biomedical engineering; embedded systems; robotics

Special Issue Information

Dear Colleagues,

Advances in data collection technology, continuing improvements of the cost advantages and processing capabilities of computing technology (according to Moore’s Law), as well as aggressive upscaling of the digital world through considerable increases in parallelism and architecture optimizations generate many potential advantages in the signal processing applied to the noisy, high-volume, high-resolution and complex data structure sets collected from different sources or sensors.

Signal processing uses different tools (autocorrelation, convolution, Fourier, cosine or wavelet transforms, adaptive filtering, linear and nonlinear estimators, compressed sensing, etc.) to solve various problems. Over time, these tools have evolved. Currently, computational signal processing uses the considerable existing computing power and machine learning approaches to solve some of the biggest challenges in many real-world technical and scientific problems. This Special Issue focuses on the latest advances in the mathematical methods used to develop state-of-the-art tools for signal processing, used to process, preserve, enhance, extract, optimize, analyze, synthesize, perceive or understand the information of interest in a better, cheaper or faster way.

In this Special Issue, the following non-exhaustive list of topics promoting solutions to the mathematical challenges in signal processing—from both fundamental and applied research perspectives—will be addressed: computer vision, medical imaging, speech, natural language processing, human–computer interaction (HCI), brain–computer interaction (BCI), graph signal processing, statistical signal processing, sparse signal processing, genomic signal processing, networks of sensors, Internet of Things (IoT), phased radar array, multi-antenna systems, cellular networks, spectrum and energy-efficient communication, multi-user signal processing, seismology, etc.

I deeply encourage you to submit your current research to be included in this Special Issue. Contributions may be submitted continuously before the deadline. After a peer-review process, submissions will be selected for publication based on their relevance and quality.

I look forward to your contribution.

Dr. Dobrea Dan-Marius
Guest Editor

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. Axioms 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 2400 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

  • computer vision
  • medical imaging
  • speech and natural language processing
  • human–computer interaction (HCI)
  • sparse signal processing
  • genomic signal processing
  • networks of sensors
  • Internet of Things (IoT)
  • phased radar arrays
  • cellular networks

Published Papers (2 papers)

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Research

12 pages, 1192 KiB  
Article
Some Properties of Zipf’s Law and Applications
by Speranta Cecilia Bolea, Mironela Pirnau, Silviu-Ioan Bejinariu, Vasile Apopei, Daniela Gifu and Horia-Nicolai Teodorescu
Axioms 2024, 13(3), 146; https://doi.org/10.3390/axioms13030146 - 23 Feb 2024
Viewed by 884
Abstract
The article extends the theoretical and applicative analysis of Zipf’s law. We are concerned with a set of properties of Zipf’s law that derive directly from the power law expression and from the discrete nature of the objects to which the law is [...] Read more.
The article extends the theoretical and applicative analysis of Zipf’s law. We are concerned with a set of properties of Zipf’s law that derive directly from the power law expression and from the discrete nature of the objects to which the law is applied, when the objects are words, lemmas, and the like. We also search for variations of Zipf’s law that can help explain the noisy results empirically reported in the literature and the departures of the empirically obtained nonlinear graph from the theoretical linear one, with the variants analyzed differing from Mandelbrot and lognormal distributions. A problem of interest that we deal with is that of mixtures of populations obeying Zipf’s law. The last problem has relevance in the analysis of texts with words with various etymologies. Computational aspects are also addressed. Full article
(This article belongs to the Special Issue Advances in Mathematical Methods in Signal Processing and Its Applications)
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11 pages, 378 KiB  
Article
Detection, Measurement and Classification of Discontinuities of Signals Captured with Noise
by Sergio Amat, Sonia Busquier and Denys Orieshkin
Axioms 2024, 13(1), 63; https://doi.org/10.3390/axioms13010063 - 19 Jan 2024
Viewed by 802
Abstract
In this work, we propose an algorithm for the detection, measurement and classification of discontinuities in signals captured with noise. Our approach is based on the Harten’s subcell-resolution approximation adapted to the presence of noise. This technique has several advantages over other algorithms. [...] Read more.
In this work, we propose an algorithm for the detection, measurement and classification of discontinuities in signals captured with noise. Our approach is based on the Harten’s subcell-resolution approximation adapted to the presence of noise. This technique has several advantages over other algorithms. The first is that there is a theory that allows us to ensure that discontinuities will be detected as long as we choose a sufficiently small discretization parameter size. The second is that we can consider different types of discretizations such as point values or cell-averages. In this work, we will consider the latter, as it is better adapted to functions with small oscillations, such as those caused by noise, and also allows us to find not only the discontinuities of the function, jumps in functions or edges in images, but also those of the derivative, corners. This also constitutes an advantage over classical procedures that only focus on jumps or edges. We present an application related to heart rate measurements used in sport as a physical indicator. With our algorithm, we are able to identify the different phases of exercise (rest, activation, effort and recovery) based on heart rate measurements. This information can be used to determine the rotation timing of players during a game, identifying when they are in a rest phase. Moreover, over time, we can obtain information to monitor the athlete’s physical progression based on the slope size between zones. Finally, we should mention that regions where heart rate measurements are abnormal indicate a possible cardiac anomaly. Full article
(This article belongs to the Special Issue Advances in Mathematical Methods in Signal Processing and Its Applications)
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