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Mathematics
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Signal Processing, Machine Learning, and Scientific Computing: Algorithms and Applications
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Signal Processing, Machine Learning, and Scientific Computing: Algorithms and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 3803

Special Issue Editors

Dr. Erik Alonso

E-Mail Website
Guest Editor
Department of Applied Mathematics, University of the Basque Country (UPV/EHU), Bilbao, Spain
Interests: biomedical signal processing; machine learning; adaptive filtering; artificial intelligence; automated algorithms during resuscitation; management of large resuscitation datasets
Dr. Aitziber Unzueta

E-Mail Website
Guest Editor
Department of Applied Mathematics, University of the Basque Country (UPV/EHU), Bilbao, Spain
Interests: operational research; algorithm development; machine learning;and artificial intelligence.

Special Issue Information

Dear Colleagues, 

Signal processing, machine learning, and scientific computing are three highly interdisciplinary and related areas that bring together engineering, applied mathematics, computer science, and statistics. Researchers in these areas contribute to the development of innovative algorithms and applications that provide a real breakthrough in a wide variety of fields, such as biomedicine, healthcare, power sector, manufacturing, logistics, supply chain, etc. The boom of big data, the technical advances in modern computers, and the massive growth of the data storage industry have only boosted research in these three areas, allowing the construction and processing of large, heterogeneous, and complex datasets that might serve to build more robust and reliable algorithms. 

The aim of this Special Issue is to invite original research manuscripts and high-quality review articles that entail scientific computing, signal processing, and machine learning for addressing challenges in many scientific fields, ranging from engineering and science to medicine and energy efficiency. Manuscripts that address healthcare and power-sector-related problems are particularly welcome for publication in this Special Issue. 

Dr. Erik Alonso
Dr. Aitziber Unzueta
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.

Keywords

  • signal processing
  • artificial intelligence
  • machine learning
  • deep learning
  • scientific computing
  • adaptive filtering
  • operational research
  • optimization algorithms
  • decision support systems
  • analytical methods
  • numerical methods
  • stochastic methods

Published Papers (3 papers)

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Research

23 pages, 6848 KiB  
Article
A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System
by Xiaolin Wang, Liyi Zhang, Yunshan Sun, Yong Zhang and Yongsheng Hu
Mathematics 2023, 11(15), 3335; https://doi.org/10.3390/math11153335 - 29 Jul 2023
Viewed by 1024
Abstract
This paper proposes a wireless channel equalization method applied to the constant modulus blind equalization system, which addresses the slow convergence and strong randomness in the initialization of equalizer weights in the constant modulus blind equalization algorithm (CMA) by introducing a hybrid arithmetic [...] Read more.
This paper proposes a wireless channel equalization method applied to the constant modulus blind equalization system, which addresses the slow convergence and strong randomness in the initialization of equalizer weights in the constant modulus blind equalization algorithm (CMA) by introducing a hybrid arithmetic whale optimization algorithm (HAWOA). The mean square error in the CMA is utilized as the cost function for the HAWOA to obtain a more effective initial weights for the equalizer. To validate the superiority of the hybrid arithmetic whale constant modulus blind equalization algorithm, tests are conducted on the equalization system using 16QAM and 64QAM signals. The simulation results demonstrate that the proposed algorithm achieved better initial weights compared to the CMA and the constant modulus blind equalization algorithm based on the whale optimization algorithm. It can obtain the desired mean square error with a lower symbol error rate in fewer iterations. Furthermore, the hybrid arithmetic whale constant modulus blind equalization algorithm exhibited faster convergence in optimizing initial weights, effectively enhancing the equalization performance of the CMA in wireless channels while ensuring timeliness. Full article
(This article belongs to the Special Issue Signal Processing, Machine Learning, and Scientific Computing: Algorithms and Applications)
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19 pages, 5761 KiB  
Article
Temperature Compensation Algorithm of Air Quality Monitoring Equipment Based on TDLAS
by Yue Wang and Xiaoli Wang
Mathematics 2023, 11(12), 2656; https://doi.org/10.3390/math11122656 - 11 Jun 2023
Cited by 2 | Viewed by 1086
Abstract
When tunable diode laser absorption spectroscopy is used to measure the concentration of gas, the second harmonic signal of demodulation is changed due to the influence of temperature change, and the error in concentration measurement is great. In order to solve the problem [...] Read more.
When tunable diode laser absorption spectroscopy is used to measure the concentration of gas, the second harmonic signal of demodulation is changed due to the influence of temperature change, and the error in concentration measurement is great. In order to solve the problem of large errors in atmospheric quality monitoring equipment due to the change in gas temperature, this paper, based on the tunable semiconductor laser absorption spectroscopy (TDLAS) theory, measured methane gas with 1000 ppm standard gas as the target and selected the central absorption wavelength of 1650 nm. The influence of temperature change on gas injection and the laser absorption spectrometer is studied. A temperature compensation algorithm based on an empirical formula is designed. Firstly, by analyzing the variable temperature test data of the detection module, it is proposed to divide the influence factors of temperature into two parts and study the influence of injection gas temperature and detector temperature, respectively. Secondly, the temperature compensation is carried out by polynomial fitting the concentration inversion results. Finally, according to the compensation effect, a scheme was proposed to compensate the measured gas by applying a constant temperature treatment to the detector at 313 K. After compensation, the average error of the system measurement is reduced from 8.4% to 1.08% when the gas temperature changes from 233 K to 343 K, which effectively reduces the deviation of the measured value caused by the abrupt temperature change. It further improves the accuracy and reliability of measuring gas concentration when gas inspection equipment is working outdoors and has strong practicability. Full article
(This article belongs to the Special Issue Signal Processing, Machine Learning, and Scientific Computing: Algorithms and Applications)
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30 pages, 8709 KiB  
Article
Identification of Cross-Country Fault with High Impedance Syndrome in Transmission Line Using Tunable Q Wavelet Transform
by Pampa Sinha, Kaushik Paul, Chidurala Saiprakash, Almoataz Y. Abdelaziz, Ahmed I. Omar, Chun-Lien Su and Mahmoud Elsisi
Mathematics 2023, 11(3), 586; https://doi.org/10.3390/math11030586 - 22 Jan 2023
Cited by 3 | Viewed by 1155
Abstract
The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the [...] Read more.
The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the risk of cross-country faults (CCFs) and high-impedance fault (HIF) syndrome in these regions. Recognizing and classifying CCFs associated with HIF syndrome is the most challenging part of the project. This study extracted signal characteristics associated with CCF and HIF syndrome using the Tunable Q Wavelet Transform (TQWT). An adaptive tunable Q-factor wavelet transform (TQWT) based feature-extraction approach for CCHIF fault signals with high impact, short response period, and broad resonance frequency bandwidth was presented. In the first part, the time–frequency distribution of the vibration signal is used to determine the distinctive frequency range. Adaptive optimal matching of the impact characteristic components in the vibration signal was achieved by optimizing the number of decomposition layers, quality factor, and redundancy of TQWT based on the characteristic frequency band. In the last, the TQWT inverse transform was utilized to recreate the best sub-band to boost its weak impact characteristics. The effectiveness of the approach is confirmed by simulation and experimental findings in signal processing. The best decomposition level for signature features that can be extracted has been decided by Minimum Description length (MDL). The IEEE 39-bus system is used to test the suggested approach with reactor switching and the Ferranti effect. Full article
(This article belongs to the Special Issue Signal Processing, Machine Learning, and Scientific Computing: Algorithms and Applications)
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