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Applied Sciences
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Application of Neural Networks in Sensors and Microwave Antennas
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Application of Neural Networks in Sensors and Microwave Antennas

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 3802

Special Issue Editors

Dr. Marcin Kolakowski

E-Mail Website
Guest Editor
Institute of Radioelectronics and Multimedia Technology, Warsaw University of Technology , Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: indoor positioning; machine learning; sensors; deep learning; radio communications
Dr. Paolo Tripicchio

E-Mail Website
Guest Editor
Institute of Mechanical Intelligence, Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56124 Pisa, Italy
Interests: robotics; haptics; control systems; virtual reality and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ljubomir Jovanov

E-Mail Website
Guest Editor
IMEC-TELIN-IPI, Ghent University, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium
Interests: sensor fusion; 3D reconstruction; real-time (3D) video noise reduction and upsampling; computer graphics and artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, neural network applications have thrived in almost every domain of modern science and technology. For this Special Issue, we invite submissions exploring recent advances in the application of neural networks in sensors and microwave antennas. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Sensor data processing;
  • Implementation of neural networks in sensor devices;
  • Machine-learning-based sensor calibration;
  • Artificial-intelligence-driven antenna design and optimization;
  • Neural-network-based smart antenna arrays;
  • Application of neural networks in MIMO antenna systems.

Works that validate the proposed methods at the experimental level will be especially welcome.

Dr. Marcin Kolakowski
Dr. Paolo Tripicchio
Dr. Ljubomir Jovanov
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. Applied Sciences 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 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

  • artificial intelligence
  • neural networks
  • sensors
  • microwave antennas
  • antenna design
  • sensor data processing

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Published Papers (3 papers)

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Research

21 pages, 5390 KiB  
Article
Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms
by Miki Vizel, Roger Alimi, Daniel Lahav, Moty Schultz, Asaf Grosz and Lior Klein
Appl. Sci. 2025, 15(2), 964; https://doi.org/10.3390/app15020964 - 19 Jan 2025
Viewed by 1052
Abstract
We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The [...] Read more.
We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yield a satisfactory mapping. To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). While LMA provided reasonable results, its reliance on a sparse sensor network and initial guesses for variables limited its accuracy. We show that the mapping is significantly improved if the data are processed with an FCNN that undergoes training and testing. Using simulations, we demonstrate that achieving similar improvement without ML would require increasing the number of sensors to more than 50. Full article
(This article belongs to the Special Issue Application of Neural Networks in Sensors and Microwave Antennas)
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17 pages, 399 KiB  
Article
Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
by Zeyu Zhang, Tianqi Chen and Yuki Todo
Appl. Sci. 2025, 15(2), 523; https://doi.org/10.3390/app15020523 - 8 Jan 2025
Viewed by 523
Abstract
This paper addresses the challenges of limited training data and suboptimal environmental conditions in image processing tasks, such as underwater imaging with poor lighting and distortion. Neural networks, including Convolutional Neural Networks (CNNs) and Transformers, have advanced image analysis but remain constrained by [...] Read more.
This paper addresses the challenges of limited training data and suboptimal environmental conditions in image processing tasks, such as underwater imaging with poor lighting and distortion. Neural networks, including Convolutional Neural Networks (CNNs) and Transformers, have advanced image analysis but remain constrained by computational demands and insufficient data. To overcome these limitations, we propose a novel split-and-concatenate method for self-attention mechanisms. By splitting Query and Key matrices into submatrices, performing cross-multiplications, and applying weighted summation, the method optimizes intermediate variables without increasing computational costs. Experiments on a real-world crack dataset demonstrate its effectiveness in improving network performance. Full article
(This article belongs to the Special Issue Application of Neural Networks in Sensors and Microwave Antennas)
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16 pages, 2455 KiB  
Article
Classification of Urea Content in Fish Using Absorbance Near-Infrared Spectroscopy and Machine Learning
by Duy Khanh Ninh, Kha Duy Phan, Thu Thi Anh Nguyen, Minh Nhat Dang, Nhan Le Thanh and Fabien Ferrero
Appl. Sci. 2024, 14(19), 8586; https://doi.org/10.3390/app14198586 - 24 Sep 2024
Cited by 2 | Viewed by 1229
Abstract
Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates [...] Read more.
Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes—Safe and Unsafe—based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%. Full article
(This article belongs to the Special Issue Application of Neural Networks in Sensors and Microwave Antennas)
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