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Machine Learning Engineering in Sensors Applications
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Machine Learning Engineering in Sensors Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 12003

Special Issue Editors

Dr. Dima Bykhovsky

E-Mail Website
Guest Editor
Electrical and Electronics Engineering Department, Shamoon College of Engineering, Beer-Sheve 8410802, Israel
Interests: wireless communication; channel modeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Natalia Vanetik

E-Mail Website
Guest Editor
Software Engineering Department, Sami Shamoon College of Engineering, Beer-Sheve 8410802, Israel
Interests: text analysis; NLP; deep learning, optimization; applications of deep learning in cyber security; integrating security and NLP
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2nd IEEE Machine Learning in Engineering Conference will be held on 3 May 2023, at the Shamoon College of Engineering (SCE), Beer Sheva campus, Israel  (https://en.sce.ac.il/news/mle_2023_conference). The MLE conference is the second in a series of conferences held at SCE dedicated to gathering academic and industrial professionals at all levels that specialize in theoretical and applicative aspects of machine learning in all fields of engineering, with a special emphasis on deep learning methods.

Machine learning and deep learning are taking hold in every area of engineering in different ways. Modern algorithms use mathematical models in a neural network to identify patterns in the data and use them for various classification, prediction, and generation tasks. This approach has been successfully applied to pattern recognition in images, videos, sounds, text, and any other type of data. With deep learning, it became possible to solve problems that were out of reach a few years ago.

The MLE 2023 conference offers keynote lectures and oral presentations covering different aspects of machine learning, including emerging technologies and theoretical models. The conference addresses both fundamental and applied, traditional and emerging issues of machine learning, such as natural language processing, image processing, and more.

Authors of selected high-quality papers from the conference that fit Sensors’ scope are invited to submit extended versions of their original submissions. In addition to the MLE 2023 papers, other independent submissions are also welcome. These contributions should have the same research topics as the ones at the conference:

  • Signal and image processing;
  • Computer vision;
  • Biomedical applications;
  • Natural language processing;
  • Robotics and control;
  • Financial applications.

Dr. Dima Bykhovsky
Dr. Natalia Vanetik
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. Sensors 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

14 pages, 641 KiB  
Article
Federated Learning-Based Spectrum Occupancy Detection
by Łukasz Kułacz and Adrian Kliks
Sensors 2023, 23(14), 6436; https://doi.org/10.3390/s23146436 - 16 Jul 2023
Cited by 4 | Viewed by 1759
Abstract
Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection’s effectiveness. Given the recent trend [...] Read more.
Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection’s effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors. Full article
(This article belongs to the Special Issue Machine Learning Engineering in Sensors Applications)
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17 pages, 823 KiB  
Article
Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression
by Wahyu Fadli Satrya and Ji-Hoon Yun
Sensors 2023, 23(2), 583; https://doi.org/10.3390/s23020583 - 4 Jan 2023
Cited by 11 | Viewed by 4401
Abstract
For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old [...] Read more.
For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases. Full article
(This article belongs to the Special Issue Machine Learning Engineering in Sensors Applications)
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12 pages, 616 KiB  
Article
Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption
by Mikhail Ronkin and Dima Bykhovsky
Sensors 2023, 23(1), 533; https://doi.org/10.3390/s23010533 - 3 Jan 2023
Cited by 2 | Viewed by 2378
Abstract
One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that [...] Read more.
One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify slight variations in the consumed current under steady-state conditions. An experimental database of current consumption signals of two similar groups containing 20 same-model computer displays was collected. The resulting signals were classified using various state-of-the-art time-series classification (TSC) methods. We successfully identified 40 similar (same-model) electrical devices with about 94% precision, while most errors were concentrated in confusion between a small number of devices. A simplified empirical wavelet transform (EWT) paired with a linear discriminant analysis (LDA) classifier was shown to be the recommended classification method. Full article
(This article belongs to the Special Issue Machine Learning Engineering in Sensors Applications)
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17 pages, 10169 KiB  
Article
Optimal Underwater Acoustic Warfare Strategy Based on a Three-Layer GA-BP Neural Network
by Zirui Wang, Jing Wu, Haitao Wang, Huiyuan Wang and Yukun Hao
Sensors 2022, 22(24), 9701; https://doi.org/10.3390/s22249701 - 11 Dec 2022
Cited by 5 | Viewed by 2762
Abstract
A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic [...] Read more.
A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic algorithm (GA) optimization, which is less accurate and less robust. Therefore, this paper proposes a method to build an optimal underwater acoustic warfare feedback system using a three-layer GA-BP neural network and dropout processing of the neural network to prevent overfitting, so that the three-layer GA-BP neural network has adequate memory capability while still having suitable generalization capability. This method improves the accuracy and stability of the defense platform in making underwater acoustic warfare strategy decisions, thus increasing the survival probability of the defense platform in the face of incoming torpedoes. This paper uses the optimal underwater acoustic warfare strategies corresponding to incoming torpedoes with different postures as the sample set. Additionally, it uses a three-layer GA-BP neural network with an overfitting treatment for training. The prediction results have less error than the typical single-layer GA-BP neural network, and the survival probability of the defense platform improves by 6.15%. This defense platform underwater acoustic warfare strategy prediction method addresses the impact on the survival probability of the defense platform due to the decision speed and accuracy. Full article
(This article belongs to the Special Issue Machine Learning Engineering in Sensors Applications)
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