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Computers
Volume 14
Issue 9
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Computers, Volume 14, Issue 9 (September 2025) – 6 articles

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31 pages, 3563 KiB  
Article
Virtual Reality for Hydrodynamics: Evaluating an Original Physics-Based Submarine Simulator Through User Engagement
by Andrei-Bogdan Stănescu, Sébastien Travadel and Răzvan-Victor Rughiniș
Computers 2025, 14(9), 348; https://doi.org/10.3390/computers14090348 (registering DOI) - 24 Aug 2025
Abstract
STEM education is constantly seeking innovative methods to enhance student learning. Virtual Reality technology can represent a critical tool for effectively teaching complex engineering subjects. This study evaluates an original Virtual Reality software application, entitled Submarine Simulator, which is developed specifically to [...] Read more.
STEM education is constantly seeking innovative methods to enhance student learning. Virtual Reality technology can represent a critical tool for effectively teaching complex engineering subjects. This study evaluates an original Virtual Reality software application, entitled Submarine Simulator, which is developed specifically to support competencies in hydrodynamics within an Underwater Engineering course at MINES Paris—PSL. Our application uniquely integrates a customized physics engine explicitly designed for realistic underwater simulation, significantly improving user comprehension through accurate real-time representation of hydrodynamic forces. The study involved a homogeneous group of 26 fourth-year engineering students, all specializing in engineering and sharing similar academic backgrounds in robotics, electronics, programming, and computer vision. This uniform cohort, primarily aged 22–28, enrolled in the same 3-month course, was intentionally chosen to minimize variations in skills, prior knowledge, and learning pace. Through a combination of quantitative assessments and Confirmatory Factor Analysis, we find that Virtual Reality affordances significantly predict user flow state (path coefficient: 0.811) which then predicts user engagement and satisfaction (path coefficient: 0.765). These findings show the substantial educational potential of tailored Virtual Reality experiences in STEM, particularly in engineering, and highlight directions for further methodological refinement. Full article
(This article belongs to the Special Issue Transformative Approaches in Education: Harnessing AI, Augmented Reality, and Virtual Reality for Innovative Teaching and Learning)
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32 pages, 565 KiB  
Article
MITM- and DoS-Resistant PUF Authentication for Industrial WSNs via Sensor-Initiated Registration
by Ashraf Alyanbaawi
Computers 2025, 14(9), 347; https://doi.org/10.3390/computers14090347 (registering DOI) - 23 Aug 2025
Abstract
Industrial Wireless Sensor Networks (IWSNs) play a critical role in Industry 4.0 environments, enabling real-time monitoring and control of industrial processes. However, existing lightweight authentication protocols for IWSNs remain vulnerable to sophisticated security attacks because of inadequate initial authentication phases. This study presents [...] Read more.
Industrial Wireless Sensor Networks (IWSNs) play a critical role in Industry 4.0 environments, enabling real-time monitoring and control of industrial processes. However, existing lightweight authentication protocols for IWSNs remain vulnerable to sophisticated security attacks because of inadequate initial authentication phases. This study presents a security analysis of Gope et al.’s PUF-based authentication protocol for IWSNs and identifies critical vulnerabilities that enable man-in-the-middle (MITM) and denial-of-service (DoS) attacks. We demonstrate that Gope et al.’s protocol is susceptible to MITM attacks during both authentication and Secure Periodical Data Collection (SPDC), allowing adversaries to derive session keys and compromise communication confidentiality. Our analysis reveals that the sensor registration phase of the protocol lacks proper authentication mechanisms, enabling attackers to perform unauthorized PUF queries and subsequently mount successful attacks. To address these vulnerabilities, we propose an enhanced authentication scheme that introduces a sensor-initiated registration process. In our improved protocol, sensor nodes generate and control PUF challenges rather than passively responding to gateway requests. This modification prevents unauthorized PUF queries while preserving the lightweight characteristics essential for resource-constrained IWSN deployments. Security analysis demonstrates that our enhanced scheme effectively mitigates the identified MITM and DoS attacks without introducing significant computational or communication overhead. The proposed modifications maintain compatibility with the existing IWSN infrastructure while strengthening the overall security posture. Comparative analysis shows that our solution addresses the security weaknesses of the original protocol while preserving its practical advantages for industrial use. The enhanced protocol provides a practical and secure solution for real-time data access in IWSNs, making it suitable for deployment in mission-critical industrial environments where both security and efficiency are paramount. Full article
(This article belongs to the Section Internet of Things (IoT) and Industrial IoT)
20 pages, 1320 KiB  
Article
A Method for Few-Shot Modulation Recognition Based on Reinforcement Metric Meta-Learning
by Fan Zhou, Xiao Han, Jinyang Ren, Wei Wang, Yang Wang, Peiying Zhang and Shaolin Liao
Computers 2025, 14(9), 346; https://doi.org/10.3390/computers14090346 - 22 Aug 2025
Abstract
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition [...] Read more.
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition method based on reinforcement metric meta-learning (RMML) is proposed. This approach, grounded in meta-learning techniques, employs transfer learning to building a feature extraction network that effectively extracts the data features under few-shot conditions. Building on this, by integrating the measurement of features of similar samples and the differences between features of different classes of samples, the metric network’s target loss function is optimized, thereby improving the network’s ability to distinguish between features of different modulation methods. The experimental results demonstrate that this method exhibits a good performance in processing new class signals that have not been previously trained. Under the condition of 5-way 5-shot, when the signal-to-noise ratio (SNR) is 0 dB, this method can achieve an average recognition accuracy of 91.8%, which is 2.8% higher than that of the best-performing baseline method, whereas when the SNR is 18 dB, the model’s average recognition accuracy significantly improves to 98.5%. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in IoT)
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47 pages, 2054 KiB  
Article
Swallow Search Algorithm (SWSO): A Swarm Intelligence Optimization Approach Inspired by Swallow Bird Behavior
by Farah Sami Khoshaba, Shahab Wahhab Kareem and Roojwan Sc Hawezi
Computers 2025, 14(9), 345; https://doi.org/10.3390/computers14090345 - 22 Aug 2025
Abstract
Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging [...] Read more.
Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging and migration. The optimization algorithm (SWSO) makes use of these behaviors to boost the ability of exploration and exploitation in the optimization process. Unlike other birds, swallows are known to be so precise when performing fast directional alterations and making intricate aerial acrobatics during foraging. Moreover, the flight patterns of swallows are very efficient; they have extensive capabilities to transition between flapping and gliding with ease to save energy over long distances during migration. This allows instantaneous changes of wing shape variations to optimize performance in any number of flying conditions. The model used by the SWSO algorithm combines these biologically inspired flight dynamics into a new computational model that is aimed at enhancing search performance in rugged terrain. The design of the algorithm simulates the swallow’s social behavior and energy-saving behavior, converting it into exploration, exploitation, control mechanisms, and convergence control. In order to verify its effectiveness, (SWSO) is applied to many benchmark problems, such as unimodal, multimodal, fixed-dimension functions, and a benchmark CEC2019, which consists of some of the most widely used benchmark functions. Comparative tests are conducted against more than 30 metaheuristic algorithms that are regarded as state-of-the-art, developed so far, including PSO, MFO, WOA, GWO, and GA, among others. The measures of performance included best fitness, rate of convergence, robustness, and statistical significance. Moreover, the use of (SWSO) in solving real-life engineering design problems is used to prove (SWSO)’s practicality and generality. The results confirm that the proposed algorithm offers a competitive and reliable solution methodology, making it a valuable addition to the field of swarm-based optimization. Full article
(This article belongs to the Special Issue Operations Research: Trends and Applications)
29 pages, 2872 KiB  
Article
Hybrid FEM-AI Approach for Thermographic Monitoring of Biomedical Electronic Devices
by Danilo Pratticò, Domenico De Carlo, Gaetano Silipo and Filippo Laganà
Computers 2025, 14(9), 344; https://doi.org/10.3390/computers14090344 - 22 Aug 2025
Abstract
Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an [...] Read more.
Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article
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19 pages, 390 KiB  
Article
Application of Partial Discrete Logarithms for Discrete Logarithm Computation
by Dina Shaltykova, Yelizaveta Vitulyova, Kaisarali Kadyrzhan and Ibragim Suleimenov
Computers 2025, 14(9), 343; https://doi.org/10.3390/computers14090343 - 22 Aug 2025
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Abstract
A novel approach to constructing an algorithm for computing discrete logarithms, which holds significant interest for advancing cryptographic methods and the applied use of multivalued logic, is proposed. The method is based on the algebraic delta function, which allows the computation of a [...] Read more.
A novel approach to constructing an algorithm for computing discrete logarithms, which holds significant interest for advancing cryptographic methods and the applied use of multivalued logic, is proposed. The method is based on the algebraic delta function, which allows the computation of a discrete logarithm to be reduced to the decomposition of known periodic functions into Fourier–Galois series. The concept of the “partial discrete logarithm”, grounded in the existence of a relationship between Galois fields and their complementary finite algebraic rings, is introduced. It is demonstrated that the use of partial discrete logarithms significantly reduces the number of operations required to compute the discrete logarithm of a given element in a Galois field. Illustrative examples are provided to demonstrate the advantages of the proposed approach. Potential practical applications are discussed, particularly for enhancing methods for low-altitude diagnostics of agricultural objects, utilizing groups of unmanned aerial vehicles, and radio geolocation techniques. Full article
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