Electronics

26 pages, 13343 KB

Open AccessArticle

Design of a Novel Negative Group Delay Circuit for Phase-Sensitive Radar System

by Xuyi Yuan, Bo Zhao and Xiaojun Liu

Electronics 2026, 15(4), 906; https://doi.org/10.3390/electronics15040906 - 23 Feb 2026

Viewed by 630

To address the degradation in he ranging accuracy of phase-sensitive radar systems caused by RF front-end group delay mismatch, this paper establishes a nonlinear group delay interference model. Using Monte Carlo simulations, we derive the group delay constraints required to achieve millimeter-level ranging [...] Read more.

To address the degradation in he ranging accuracy of phase-sensitive radar systems caused by RF front-end group delay mismatch, this paper establishes a nonlinear group delay interference model. Using Monte Carlo simulations, we derive the group delay constraints required to achieve millimeter-level ranging accuracy, and, based on these constraints, we propose a novel negative group delay circuit (NGDC). The proposed NGDC attains a figure of merit (FoM) of 0.063, outperforming related designs while offering low insertion loss and high tuning flexibility. After cascading the NGDC, the group delay of a 200–400 MHz bandpass filter (BPF) was improved from

3.015 \pm 1.135

ns to

2.53 \pm 0.54

ns. When incorporated into the radar system, the NGDC reduces the root mean square (RMS) ranging error from 17.17 mm to 9.25 mm at

SNR = 16

dB, approaching the theoretical limit of 5.07 mm. These results demonstrate that the proposed hardware provides effective support for high-precision phase linearization in radar systems and offers substantial engineering value. Full article

(This article belongs to the Section Circuit and Signal Processing)

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30 pages, 1870 KB

Open AccessArticle

DL-MFFSSnet: A Multi-Feature Fusion-Based Dynamic Collaborative Spectrum Sensing Method in a Satellite–Terrestrial Converged System

by Chao Tang, Yueyun Chen, Guang Chen, Liping Du, Zhen Wang and Huan Liu

Electronics 2026, 15(4), 905; https://doi.org/10.3390/electronics15040905 - 23 Feb 2026

Viewed by 507

Abstract

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink [...] Read more.

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Next-Generation Wireless Networks: Advances in 5G, 6G, and Broadband Communication)

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20 pages, 554 KB

Open AccessArticle

RIS-Assisted Physical Layer Security for Cell-Free ISAC Systems

by Na Chen, Guijie Lin, Rubing Jian, Yusheng Wang, Jianquan Wang, Lei Sun, Wei Li, Minoru Okada, Qu Wang, Tao Gu and Changyuan Yu

Electronics 2026, 15(4), 904; https://doi.org/10.3390/electronics15040904 - 23 Feb 2026

Viewed by 995

Abstract

Physical layer security (PLS) is a fundamental challenge for sixth-generation (6G) wireless networks, particularly in integrated sensing and communication (ISAC) systems, where sensing targets may simultaneously act as potential eavesdroppers. In this paper, we investigate PLS in a reconfigurable intelligent surface (RIS)-assisted cell-free [...] Read more.

Physical layer security (PLS) is a fundamental challenge for sixth-generation (6G) wireless networks, particularly in integrated sensing and communication (ISAC) systems, where sensing targets may simultaneously act as potential eavesdroppers. In this paper, we investigate PLS in a reconfigurable intelligent surface (RIS)-assisted cell-free ISAC system, where distributed access points collaboratively serve users and actively sense potential eavesdroppers. We formulate a weighted sum secrecy rate maximization problem through joint ISAC beamforming design. The resulting non-convex problem is first transformed into a semidefinite programming (SDP) formulation and then solved via convex optimization techniques. To further enhance secure communication performance, we extend the framework by incorporating RIS phase shift optimization and propose an alternating optimization algorithm that jointly optimizes active ISAC beamforming and passive RIS configurations. This joint design exploits the controllable wireless propagation environment provided by RISs to enhance legitimate links while suppressing eavesdropping channels. Extensive simulation results demonstrate that the proposed approach significantly outperforms baseline approaches. Specifically, the proposed joint ISAC method improves the communication signal to interference plus noise ratio (SINR) by approximately

1.8

dB and the sensing signal-noise ratio (SNR) by

4.8

dB compared to sensing-priority and communication-priority baselines, respectively. Furthermore, the RIS-assisted framework improves a weighted sum secrecy rate gain of approximately

2.2

dB compared to the frameworks without RIS, validating the proposed framework as a promising solution for secure and spectrum-efficient cell-free ISAC systems in future 6G networks. Full article

(This article belongs to the Special Issue Security for Integrated Sensing and Communication in Next-Generation Wireless Communication)

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36 pages, 7369 KB

Open AccessArticle

Prompt-Driven Development with Claude Code: Developing a TUI Framework for the Ring Programming Language

by Mahmoud Samir Fayed and Ahmed Samir Fayed

Electronics 2026, 15(4), 903; https://doi.org/10.3390/electronics15040903 - 23 Feb 2026

Viewed by 3052

Abstract

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for [...] Read more.

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for the Ring programming language using a prompt-driven workflow with Claude Code (Opus 4.5), employing an iterative testing and corrective feedback. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction (35 prompts), followed by complex UI systems (25 prompts) and control expansion (20 prompts). Bug-related prompts covered redraw issues, event-handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window-manager capabilities, and advanced UI components. Most prompts were brief (mean ≈ 258 characters; median = 207 characters), reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behavior, and issuing corrective prompts—without writing any code manually. The resulting framework contains 28 classes, 334 methods and includes a windowing subsystem, event-driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi-window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behavior, this study provides empirical evidence that modern LLMs can preserve architectural coherence across iterations and support the construction of new libraries and tools for emerging programming languages, highlighting prompt-driven development as a viable methodology within software-engineering practice. Full article

(This article belongs to the Section Computer Science & Engineering)

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14 pages, 449 KB

Open AccessArticle

Graph Contrastive Learning via Noisy Training for Cold-Start Recommendation

by Tingting Fang, Guicheng Shen and Qiurui Sun

Electronics 2026, 15(4), 902; https://doi.org/10.3390/electronics15040902 - 23 Feb 2026

Viewed by 636

Abstract

This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance [...] Read more.

This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance of noisy user-item interactions; however, they can achieve effective performance only on existing user–item interactions, which are not cold-start interactions. The question of how to find an optimal graph contrastive learning method that is suitable for cold-start cases still remains to be explored. We propose a novel method, graph contrastive learning via noisy training (GCLNT), to alleviate the cold-start recommendation problem. Specifically, GCLNT identifies user–item interactions with different preferences, and assigns them to different preference environments. With such different preference environments, noisy training is used to enhance the model’s robustness. We evaluate GCLNT on three datasets, and the results demonstrate the effectiveness of GCLNT in handling the cold-start in recommender systems. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond)

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21 pages, 4060 KB

Open AccessArticle

Machine Learning and Regression-Based Multimodal Intelligent Injury Severity Modeling of Median Crossover Crashes

by Deo Chimba, Sandeep Bist, Jeannine Mbabazi, Philbert Mwandepa and Wittness Mariki

Electronics 2026, 15(4), 901; https://doi.org/10.3390/electronics15040901 - 23 Feb 2026

Viewed by 536

Abstract

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median [...] Read more.

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median barrier performance in Tennessee by integrating structured crash data, roadway and traffic characteristics, post-impact vehicle responses, and unstructured police narratives. Across 6094 crashes on 576 cable barrier segments, 1196 involved barrier impacts and 914 included complete post-impact response information. Deep learning-based text mining using a BERT transformer model was applied to narrative descriptions from fatal, serious injury, and minor injury crashes to extract contextual indicators of loss of control, impact dynamics, and injury mechanisms. Safety effectiveness evaluation using Empirical Bayes methods showed substantial reductions after installation, including a 96% decrease in fatal crashes and an 88% reduction in serious-injury crashes. Vehicle–barrier interactions—classified as containment, redirection, rollover, or penetration—were modeled using a multinomial logit framework with marginal effects to assess the influence of geometric, operational, and vehicle-related factors. Reduced barrier offset, narrow shoulders, high traffic volumes, outer-lane departures, and heavy-vehicle involvement significantly increased the likelihood of rollover and penetration events, which are strongly linked to higher injury severity. Through fusing multimodal data and combining explainable statistical models with deep learning text analysis, this study provided a scalable, trustworthy approach to characterizing injury risk, aligning transportation safety analytics with emerging intelligent healthcare and big-data methodologies aimed at preventing severe and fatal trauma. Full article

(This article belongs to the Special Issue Multimodal Intelligent Healthcare and Big Data Analysis)

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Graphical abstract

26 pages, 9016 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Integration of Hybrid Prefilter and Corner Trajectory Planning for Simultaneously Suppressing Residual Vibration and Reducing Cornering Error of SCARA Robots

by Syh-Shiuh Yeh and Ming-Han You

Electronics 2026, 15(4), 900; https://doi.org/10.3390/electronics15040900 - 23 Feb 2026

Viewed by 470

Abstract

During high-speed cornering, the motion accuracy and efficiency of SCARA robots are often compromised by residual vibrations and cornering errors. Conventional control methods often fail to address these two coupled problems simultaneously. Therefore, this study developed an integrated design strategy to simultaneously suppress [...] Read more.

During high-speed cornering, the motion accuracy and efficiency of SCARA robots are often compromised by residual vibrations and cornering errors. Conventional control methods often fail to address these two coupled problems simultaneously. Therefore, this study developed an integrated design strategy to simultaneously suppress residual vibrations and restrict cornering errors for improving the cornering performance of the SCARA robot. The core of this design strategy is to develop a hybrid prefilter via the convolution of an input shaper and a finite impulse response filter, thereby creating a prefilter with robust, high-performance residual vibration suppression. Subsequently, to accommodate the asymmetric acceleration and deceleration generated by the hybrid prefilter, this study developed a systematic corner trajectory planning method that can calculate the cornering trajectory parameters based on a preset value of the cornering error to restrict the cornering error and ensure the cornering accuracy of the SCARA robot. Experimental results indicated that under the condition of a restricted cornering error, the developed hybrid prefilter can reduce residual vibration by >85%. Thus, the hybrid prefilter designed with the corner trajectory planning method can mitigate the coupled problem of residual vibration and cornering error, suppressing the residual vibration without compromising cornering accuracy. Full article

(This article belongs to the Special Issue Intelligent Perception and Control for Robotics)

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19 pages, 2483 KB

Open AccessArticle

Parallel Axial Attention and ResNet-Based Bearing Fault Diagnosis Method

by Haitao Wang, Guozhi Fang, Xiaolong Cui and Xin An

Electronics 2026, 15(4), 899; https://doi.org/10.3390/electronics15040899 - 22 Feb 2026

Viewed by 546

Abstract

To address the limitations of traditional rolling bearing fault diagnosis methods—such as inadequate feature extraction, limited noise robustness, and weak generalization under variable working environments—this study proposes a fault diagnosis framework that integrates parallel axial attention into a ResNet architecture. First, continuous wavelet [...] Read more.

To address the limitations of traditional rolling bearing fault diagnosis methods—such as inadequate feature extraction, limited noise robustness, and weak generalization under variable working environments—this study proposes a fault diagnosis framework that integrates parallel axial attention into a ResNet architecture. First, continuous wavelet transform (CWT), known for its inherent noise immunity, is employed to convert vibration signals into time–frequency images, providing a noise-suppressed representation of fault characteristics. Convolutional layers are then applied to reduce image dimensionality and computational complexity. A parallel axial attention module is subsequently introduced to independently capture feature dependencies along the temporal and frequency axes, enhancing the model’s ability to focus on discriminative fault-related regions while filtering out irrelevant noise. ResNet serves as the backbone network for deep feature learning and classification. Experiments on the Case Western Reserve University bearing dataset show that the proposed method achieves an average diagnostic accuracy exceeding 99.67% under multiple operating regimes. Notably, it maintains an accuracy above 95% even in high-noise environments with signal-to-noise ratios (SNRs) ranging from −4 dB to 4 dB, significantly outperforming several existing convolutional neural network-based approaches. This demonstrates the strong anti-noise capability and robustness resulting from the synergistic combination of time–frequency analysis and attention mechanisms. Furthermore, cross-dataset validation using the Southeast University bearing dataset confirms the strong generalization ability of the method. These results indicate that the proposed approach exhibits excellent diagnostic performance and practical applicability in noisy and complex industrial environments. Full article

(This article belongs to the Special Issue Deep Learning in Image Processing and Pattern Recognition, 2nd Edition)

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25 pages, 9165 KB

Open AccessArticle

Lightweight Network Design for Joint Detection and Modulation Recognition of LPI Radar Signals with Knowledge Distillation

by Zixuan Wang, Quan Zhao, Yuandong Shi, Chang Sun and Xiongkui Zhang

Electronics 2026, 15(4), 898; https://doi.org/10.3390/electronics15040898 - 22 Feb 2026

Viewed by 449

Abstract

In the field of electronic support and radar warning, it is necessary to effectively detect and recognize the modulation types of non-cooperative radar signals, especially for radars with Low Probability of Intercept (LPI) waveforms. Multiple intelligent detection and recognition algorithms based on the [...] Read more.

In the field of electronic support and radar warning, it is necessary to effectively detect and recognize the modulation types of non-cooperative radar signals, especially for radars with Low Probability of Intercept (LPI) waveforms. Multiple intelligent detection and recognition algorithms based on the Transformer architecture have been proposed, achieving good performance even under low signal-to-noise ratio (SNR). However, Transformer-based radar intelligent detection and recognition algorithms have a huge number of parameters coupled with complex structures, which will result in significant power consumption and computational latency when deployed on general computing platforms. To address the above issues, this paper proposes a lightweight design for Transformer-based radar signal intelligent detection and recognition networks. A Lightweight Joint Detection and Modulation Recognition Networks (JDMR-LNet) is designed. To enhance the feature extraction ability of lightweight networks, this paper designed a hybrid model distillation method. The experimental results demonstrate that, compared with the directly trained JDMR-LNet, the accuracy of automatic modulation type recognition of the JDMR-LNet after distillation is increased by 2.37% at −12 dB, and the signal detection is increased by 2.07% at −10 dB. The number of parameters of the JDMR-LNet has also decreased significantly. Compared with the original model, the JDMR-LNet is compressed by 11.18 times. Furthermore, this paper completed FPGA deployment of the JDMR-LNet model, with simulation verifying its functional correctness. Full article

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23 pages, 3498 KB

Open AccessArticle

Design and Control of a Modular High-Gain DC–DC Converter with Extensible Switched-Inductor Cells

by Christopher Jesus Rodriguez-Cortes, Panfilo R. Martinez-Rodriguez, Diego Langarica-Cordoba, Alejandro Rolan-Blanco, Gerardo Vazquez-Guzman, Juan Antonio Villanueva-Loredo and Jose Miguel Sosa

Electronics 2026, 15(4), 897; https://doi.org/10.3390/electronics15040897 - 22 Feb 2026

Cited by 1 | Viewed by 553

Abstract

DC–DC converters have become a key component in the structure of renewable energy systems, where an interface to increase and regulate the output voltage is required. This paper presents a modular non-isolated topology that achieves high voltage gain through interconnected switched-inductor cells. For [...] Read more.

DC–DC converters have become a key component in the structure of renewable energy systems, where an interface to increase and regulate the output voltage is required. This paper presents a modular non-isolated topology that achieves high voltage gain through interconnected switched-inductor cells. For the proposed converter, the design rules for sizing the energy storage elements for n number of cells are obtained, considering continuous, discontinuous, and boundary operation modes. Therefore, design equations are provided to support the precise selection of passive components according to voltage and power specifications. A nonlinear dynamic model is developed, and a model-based control scheme with inner current and outer voltage loops ensures robust regulation and fast transient response. Experimental validation on a 200 W prototype confirms theoretical predictions under steady-state and real-life dynamic conditions. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Electrical Power Conversion and Advanced Control)

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18 pages, 56175 KB

Open AccessFeature PaperArticle

Enhanced Three-Dimensional Double Random Phase Encryption: Overcoming Phase Information Loss in Zero-Amplitude Singularities for Simultaneous Two Primary Data

by Myungjin Cho and Min-Chul Lee

Electronics 2026, 15(4), 896; https://doi.org/10.3390/electronics15040896 - 22 Feb 2026

Viewed by 389

Abstract

This paper proposes an advanced three-dimensional optical encryption technique based on double random phase encryption for the simultaneous encryption of two primary datasets. While conventional double random phase encryption offers high-speed encryption, it suffers from low data efficiency. To address this issue, the [...] Read more.

This paper proposes an advanced three-dimensional optical encryption technique based on double random phase encryption for the simultaneous encryption of two primary datasets. While conventional double random phase encryption offers high-speed encryption, it suffers from low data efficiency. To address this issue, the proposed method assigns the first primary dataset to the amplitude and the second to the phase. However, this approach faces a critical limitation: the phase information becomes undefined or lost when the amplitude is zero. Therefore, we introduce a biased amplitude encoding scheme for double random phase encryption to ensure the mathematical recoverability of the phase component. In the proposed method, a biased value

ϵ

is added to the amplitude part during the double random phase encryption encryption process and subsequently subtracted from the decrypted data to recover the two primary datasets. To verify the effectiveness of our approach, we employ synthetic aperture integral imaging and volumetric computational reconstruction. The experimental results show that while the first dataset remains lossless, the lossy characteristics of the second dataset are significantly mitigated. Full article

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15 pages, 1538 KB

Open AccessArticle

A Hybrid-Driven Fault Diagnosis Method for Railway Freight Car Braking System

by Yanhui Bai, Honghui Li, Guoliang Gong, Nahao Shen and Yi Xu

Electronics 2026, 15(4), 895; https://doi.org/10.3390/electronics15040895 - 21 Feb 2026

Viewed by 451

Abstract

With the increasing demand for heavy-haul railway freight, both the number and volume of heavy-haul freight cars continue to grow. As the core system of railway freight transportation, the reliable operation of the brake system is fundamental to ensuring train safety. The freight [...] Read more.

With the increasing demand for heavy-haul railway freight, both the number and volume of heavy-haul freight cars continue to grow. As the core system of railway freight transportation, the reliable operation of the brake system is fundamental to ensuring train safety. The freight car braking system fault diagnosis model, which relies on historical data while failing to account for changes in braking curves when locomotives are coupled with different vehicles, is the main reason why early failures of the braking system are not diagnosed. Consequently, real-time monitoring of the freight car braking system and early fault diagnosis have emerged as a pivotal technical challenge that necessitates resolution within the framework of the railway freight maintenance reform. This paper proposes a novel hybrid-driven prediction method that effectively combines Convolutional Neural Networks, Adaptive Radial Basis Function Neural Networks, and Extreme Learning Machines (CARE). To achieve comprehensive fault feature extraction, based on CNN of the image data classification, the K-means clustering algorithm is introduced to adaptively initialize the radial basis centers of the RBF and recalculate the radial basis radii. Moreover, to improve the real-time performance and accuracy of fault diagnosis, the network layers are expanded, and the ELM algorithm is employed to construct an optimization strategy for high-dimensional data processing in the network layers. The experimental results demonstrate that when considering the coupling of different vehicles in the railway freight car, the proposed CARE model exhibits faster convergence speed and significantly improves the effectiveness and real-time performance of fault diagnosis in the railway freight car braking system. Full article

(This article belongs to the Special Issue Advancements in Predictive Maintenance—Fault Detection and Condition Monitoring in Electromechanical Systems)

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29 pages, 770 KB

Open AccessArticle

Revisiting SMS Spam Detection: The Impact of Feature Representation on Classical Machine Learning Models

by Meryem Soysaldı Şahin, Durmuş Özkan Şahin and Areej Fateh Salah

Electronics 2026, 15(4), 894; https://doi.org/10.3390/electronics15040894 - 21 Feb 2026

Viewed by 993

Abstract

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature [...] Read more.

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature representation under imbalanced short-text conditions.In practical SMS filtering systems, minimizing false positives (i.e., incorrectly blocking legitimate messages) is a critical operational constraint. Therefore, beyond overall accuracy, precision and specificity are emphasized to ensure reliable preservation of legitimate communication. Using the SMSSpamCollection dataset (5574 messages: 747 spam and 4827 ham), seven feature representation techniques were evaluated in combination with six widely adopted classifiers, resulting in 42 configurations assessed under 10-fold cross-validation. The results demonstrate that feature representation plays a more critical role than classifier complexity. Character-level 3-grams combined with Logistic Regression achieved the best overall performance, reaching 98.55% accuracy, with 98.55% precision and 90.50% recall for the spam class (F1-score = 94.32%), and 0.9893 AUC. Linear SVM produced comparable results, highlighting the effectiveness of linear models when paired with expressive representations. Beyond reporting performance metrics, this study analyzes feature–classifier interaction patterns and clarifies practical trade-offs between precision, recall, and computational efficiency. The findings provide reproducible baselines and structured guidance for designing efficient SMS spam filtering systems. Full article

(This article belongs to the Topic Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence)

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22 pages, 4902 KB

Open AccessArticle

A Coherent Difference Imaging Method for Antenna Decoupling in Ground-Penetrating Radar

by Zihao Wang, Shengbo Ye, Yang Xu, Menghao Zhu, Yicai Ji, Xiaojun Liu, Guangyou Fang and Yudong Fang

Electronics 2026, 15(4), 893; https://doi.org/10.3390/electronics15040893 - 21 Feb 2026

Viewed by 552

Abstract

Ground-penetrating radar (GPR) is a key non-destructive technique for subsurface reconstruction, widely valued for its ability to image buried structures without disruption. Among its various implementations, vehicle-mounted GPR has emerged as particularly suitable for highway tunnel assessment due to its rapid non-contact operation. [...] Read more.

Ground-penetrating radar (GPR) is a key non-destructive technique for subsurface reconstruction, widely valued for its ability to image buried structures without disruption. Among its various implementations, vehicle-mounted GPR has emerged as particularly suitable for highway tunnel assessment due to its rapid non-contact operation. However, current systems are often constrained by closely spaced antennas that generate strong direct coupling and consequently limit detection depth. To mitigate this issue, this paper proposes an antenna decoupling method based on coherent difference imaging. A differential decoupling model is first established to characterize the relationship between conventional transceiver signals and the derived differential signals, explicitly accounting for parameters such as antenna height and target depth. Furthermore, a coherent difference imaging algorithm is developed, employing a sliding-window coherence process to resolve dual-peak artifacts and restore focused target images. Simulations validate consistent performance across varying antenna heights, while experiments demonstrate over 37.2 dB isolation in the 1–3 GHz band and markedly improved imaging focus compared to conventional configurations, thereby enhancing buried target detection and supporting reliable data interpretation. Full article

(This article belongs to the Special Issue Advanced RF, Microwave, and Millimeter-Wave Antennas, Devices, and Systems)

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31 pages, 785 KB

Open AccessArticle

A Multimodal AI System: Comparing LLMs and Theorem Proving Systems

by Phillip G. Bradford and Henry Orphys

Electronics 2026, 15(4), 892; https://doi.org/10.3390/electronics15040892 - 21 Feb 2026

Viewed by 883

Abstract

This paper discusses a multimodal AI system applied to legal reasoning for tax law. The results given here are very general and apply to systems developed for other areas besides tax law. A central goal of this work is to gain a better [...] Read more.

This paper discusses a multimodal AI system applied to legal reasoning for tax law. The results given here are very general and apply to systems developed for other areas besides tax law. A central goal of this work is to gain a better understanding of the relationships between LLMs (Large Language Models) and automated theorem-proving methodologies. To do this, we suppose (1) two cases for the theorem-proving system: one where it has a countable number of total meanings for its countable number of atoms and the other is where it has an uncountable number of total meanings for its countable number of atoms, and (2) LLMs can have an uncountable number of token meanings. With this in mind, the results given in this paper use the downward and upward Löwenheim–Skolem theorems and logical model theory to contrast these two AI modalities. One modality focuses on syntactic proofs and the other focuses on logical semantics based on LLMs. Particularly, one modality uses a rule-based first-order logic theorem-proving system to perform legal reasoning. The objective of this theorem-proving system is to provide proofs as evidence of valid legal reasoning when enacted laws are applied to particular situations. These proofs are syntactic structures that can be presented in the form of narrative explanations of how the answer to the legal question was determined. The second modality uses LLMs to analyze and transform a user’s tax query so this query can be sent to a first-order logic theorem-proving system to perform its legal reasoning function. The main goal of our application of LLMs is to enhance and simplify user input and output for the theorem-proving system. Using logical model theory, we show how there can exist an equivalence between laws represented in logic of the theorem-proving system, fixed in time when the theorem-proving system was set up, and new semantics given by LLMs. These results are based on logical model theory and Löwenheim–Skolem theorems. Full article

(This article belongs to the Section Computer Science & Engineering)

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25 pages, 3276 KB

Open AccessArticle

SIDWA: Synthetic Image Detection Based on Discrete Wavelet Transform Stem and Deformable Sliding Window Cross-Attention

by Luo Li, Tianyi Lu, Jiaxin Song and Ke Cheng

Electronics 2026, 15(4), 891; https://doi.org/10.3390/electronics15040891 - 21 Feb 2026

Viewed by 547

Abstract

With the rapid evolution of Generative Adversarial Networks (GANs) and diffusion models (DMs), the detection of synthetic images faces significant challenges due to non-rigid artifacts and complex frequency biases. In this paper, we propose SIDWA, a novel dual-branch detection framework that leverages the [...] Read more.

With the rapid evolution of Generative Adversarial Networks (GANs) and diffusion models (DMs), the detection of synthetic images faces significant challenges due to non-rigid artifacts and complex frequency biases. In this paper, we propose SIDWA, a novel dual-branch detection framework that leverages the synergy between frequency and spatial domains. Within the spatial branch, we design a Deformable Sliding Window Cross-Attention (DSWA) module, which utilizes a learnable offset mechanism to dynamically warp the receptive field, effectively capturing distorted edges and non-linear texture features. Simultaneously, the Discrete Wavelet Transform (DWT) Stem decomposes input images into multi-scale sub-bands to preserve crucial high-frequency residues. Through a Frequency-Semantic Resonance Projector (FSRP) strategy, the semantic priors from the spatial branch act as queries to guide the model toward localized frequency anomalies, achieving a unified “where to look” and “how to analyze” approach. Experimental results for the SIDataset (SIDset) benchmark demonstrate that Synthetic Image Detection based on Discrete Wavelet Transform Stem and Deformable Sliding Window Cross-Attention (SIDWA) achieves superior performance, with an average accuracy exceeding 95% and a competitive inference time of 18.2 ms on an NVIDIA A100 GPU. Ablation studies further validate the critical role of learnable offsets and frequency integration in enhancing robustness and generalization. SIDWA offers an efficient and reliable forensic solution for combating the growing threats of sophisticated generative forgeries. Full article

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19 pages, 4360 KB

Open AccessArticle

Fast and Accurate Source Reconstruction for TSV-Based Chips via Contribution-Driven Dipole Pruning

by Hao Cheng, Weimin Wang, Yongle Wu and Keyan Li

Electronics 2026, 15(4), 890; https://doi.org/10.3390/electronics15040890 - 21 Feb 2026

Viewed by 534

Abstract

Electromagnetic compatibility (EMC) diagnostics for high-density through-silicon via (TSV)-based chips face significant challenges due to complex three-dimensional electromagnetic coupling and inefficient source reconstruction workflows. This paper proposes a universal contribution-driven dipole preprocessing technique tailored for dipole array-based source reconstruction methods, addressing the critical [...] Read more.

Electromagnetic compatibility (EMC) diagnostics for high-density through-silicon via (TSV)-based chips face significant challenges due to complex three-dimensional electromagnetic coupling and inefficient source reconstruction workflows. This paper proposes a universal contribution-driven dipole preprocessing technique tailored for dipole array-based source reconstruction methods, addressing the critical efficiency-accuracy trade-off inherent in traditional approaches. The core innovation is an influence factor-based evaluation-elimination mechanism that extracts effective dipole components aligned with the structural characteristics of TSV-based chips and multilayer printed circuit boards, while eliminating redundant dipoles independently of the downstream source reconstruction algorithm. Validation on a multilayer PCB (1 GHz) and a TSV-based chip (4 GHz) demonstrates that the technique maintains high reconstruction accuracy, with error increase limited to ≤0.2% for the simulated PCB and ≤0.05% for the physically measured TSV-based chip. Computational time is reduced by 28–61% for the PCB and 20–28% for the TSV chip compared to traditional source reconstruction without preprocessing. For TSV-based chips exhibiting complex electromagnetic behavior, the technique delivers consistent performance across different dipole configurations, providing a fast, robust, and universal EMC diagnostic tool for high-density electronic devices. Full article

(This article belongs to the Section Microelectronics)

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19 pages, 1215 KB

Open AccessArticle

On the Dynamics of Ergonomic Load in Biomimetic Self-Organizing Systems

by Nikitas Gerolimos, Vasileios Alevizos and Georgios Priniotakis

Electronics 2026, 15(4), 889; https://doi.org/10.3390/electronics15040889 - 21 Feb 2026

Viewed by 478

Abstract

Traditional ergonomic considerations in human–machine and human–swarm systems have primarily relied on static diagnostic snapshots, which often fail to capture the temporal accumulation and non-linear dissipation of musculoskeletal fatigue. As Industry 5.0 transitions toward immersive, human-centric cyber-physical systems, redefining ergonomic load as an [...] Read more.

Traditional ergonomic considerations in human–machine and human–swarm systems have primarily relied on static diagnostic snapshots, which often fail to capture the temporal accumulation and non-linear dissipation of musculoskeletal fatigue. As Industry 5.0 transitions toward immersive, human-centric cyber-physical systems, redefining ergonomic load as an endogenous state variable allows for real-time control of musculoskeletal integrity. This work proposes the Dynamic Integrity Governor (DIG) framework, which treats ergonomic load as a normalized, dimensionless state variable

ξ (t)

that evolves according to a stochastic proxy of recursive Newton–Euler dynamics. Leveraging a machine-perception-aware Adaptive Event-Triggered Mechanism (AETM) and the Multi-modal Flamingo Search Algorithm (MMFSA), we develop a decentralized architecture that redistributes ergonomic demands in real-time. The framework utilizes a 7-DOF kinematic model and Control Barrier Functions (CBF) to maintain human–swarm interaction within safe biomechanical boundaries, effectively filtering stochastic sensor noise through Girard-based stability buffers. Computational validation via N = 1000 Monte Carlo runs demonstrates that the proposed strategy achieves a 79.97% reduction in control updates (SD = 0.19%; p < 0.0001; Cohen’s d = 2.41), ensuring a positive minimum inter-event time (MIET) to prevent the Zeno phenomenon and supporting carbon-aware AI operations. The integration of variable prediction horizons yields an 80.69% improvement in solving time, while ensuring a minimal computational footprint suitable for real-time edge deployment. The identification of optimal postural niches maintains peak ergonomic load at 41.42%, representing a significant safety margin relative to the integrity barrier. While validated against a 50th percentile male profile, the DIG framework establishes a modular foundation for personalized ergonomic governors in inclusive Industry 5.0 applications. Full article

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22 pages, 1271 KB

Open AccessEditor’s ChoiceArticle

Leveraging MCP and Corrective RAG for Scalable and Interoperable Multi-Agent Healthcare Systems

by Dimitrios Kalathas, Andreas Menychtas, Panayiotis Tsanakas and Ilias Maglogiannis

Electronics 2026, 15(4), 888; https://doi.org/10.3390/electronics15040888 - 21 Feb 2026

Viewed by 1056

Abstract

The rapid evolution of Generative AI (GenAI) has created the conditions for developing innovative solutions that disrupt all fields of human-related activities. Within the healthcare sector, numerous AI-driven applications have emerged, offering comprehensive health-related insights and addressing user questions in real time. Nevertheless, [...] Read more.

The rapid evolution of Generative AI (GenAI) has created the conditions for developing innovative solutions that disrupt all fields of human-related activities. Within the healthcare sector, numerous AI-driven applications have emerged, offering comprehensive health-related insights and addressing user questions in real time. Nevertheless, most of them use general-purpose Large Language Models (LLMs); consequently, the responses may not be as accurate as required in clinical settings. Therefore, the research community is adopting efficient architectures, such as Multi-Agent Systems (MAS) to optimize task allocation, reasoning processes, and system scalability. Most recently, the Model Context Protocol (MCP) has been introduced; however, very few applications apply this protocol within a healthcare MAS. Furthermore, Retrieval-Augmented Generation (RAG) has proven essential for grounding AI responses in verified clinical literature. This paper proposes a novel architecture that integrates these technologies to create an advanced Agentic Corrective RAG (CRAG) system. Unlike standard approaches, this method incorporates an active evaluation layer that autonomously detects retrieval failures and triggers corrective fallback mechanisms to ensure safety and accuracy. A comparative analysis was conducted for this architecture against Typical RAG and Cache-Augmented Generation (CAG), demonstrating that the proposed solution improves workflow efficiency and enables more accurate, context-aware interventions in healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)

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19 pages, 56435 KB

Open AccessArticle

Deep-Guided Dual-Task Collaborative Learning for Oriented Object Detection in Remote Sensing Images

by Jing Bai, Caizhi Gu, Haiyang Hu, Congcong Li, Yuqi Jiang, Yanran Dai, Zhengyou Wang and Shanna Zhuang

Electronics 2026, 15(4), 887; https://doi.org/10.3390/electronics15040887 - 21 Feb 2026

Cited by 1 | Viewed by 538

Abstract

Object detection, as a fundamental task, forms the cornerstone of intelligent applications in both UAV surveillance and satellite remote sensing. While most prior works concentrate on solving object scale and rotation angle variance caused by altitude changes, the spatial misalignment stemming from the [...] Read more.

Object detection, as a fundamental task, forms the cornerstone of intelligent applications in both UAV surveillance and satellite remote sensing. While most prior works concentrate on solving object scale and rotation angle variance caused by altitude changes, the spatial misalignment stemming from the differing demands of classification subtask and regression subtask also plays a critical role. To tackle these problems, a novel deep-guided dual-task collaborative learning framework is proposed. This framework integrates two key modules: deep-guided collaborative feature fusion (DGC-FF) and dual-task collaborative feature alignment (DTC-FA). DGC-FF effectively integrates fine-grained spatial and semantic information to enhance the network’s multi-scale perception capability. DTC-FA alleviates spatial misalignment between classification and regression branches through collaborative feature alignment and incorporates a rotation-aware detection branch to adapt to varying object orientations. Experimental results show that the proposed method achieves mAP@0.5 of 79.3% on the DroneVehicle dataset and mAP@0.5 of 81.6% on the DIOR-R dataset. The proposed method not only outperforms all compared methods in accuracy but also strikes a favorable efficiency–accuracy balance with an inference rate of 55–58 FPS. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 1920 KB

Open AccessArticle

Delving into Unreliable Pseudo-Labels for Semi-Supervised Medical Image Segmentation via Conformal Selection

by Jialin Shi, Zongyao Yang, Youquan Yang, Kai Wu and Zongjie Wang

Electronics 2026, 15(4), 886; https://doi.org/10.3390/electronics15040886 - 20 Feb 2026

Viewed by 759

Abstract

Semi-supervised medical image segmentation has recently achieved great success, but assigning trustworthy pseudo-labels to unlabeled images has been a difficult problem in medical image processing. A common solution is to select reliable predicted pixels as the pseudo-labels. However, unreliable pixels are often concentrated [...] Read more.

Semi-supervised medical image segmentation has recently achieved great success, but assigning trustworthy pseudo-labels to unlabeled images has been a difficult problem in medical image processing. A common solution is to select reliable predicted pixels as the pseudo-labels. However, unreliable pixels are often concentrated in the edge areas of the foreground and background in medical tasks. Directly discarding these pixels will result in this important information never being available. The foreground of medical images is usually surrounded by the edge area. This section of pixels is a mixture of the two categories, which makes it very difficult to distinguish. To address these problems, we propose a semi-supervised medical segmentation framework that combines conformal prediction and contrastive learning. Our framework can use conformal prediction to select pseudo-labels with high confidence and preserve important boundary information. Furthermore, the segmentation performance of edge regions can be improved using contrastive learning between edge categories and non-edge categories. Extensive experiments on multiple benchmarks show that our framework consistently outperforms state-of-the-art methods. Full article

(This article belongs to the Section Artificial Intelligence)

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24 pages, 5558 KB

Open AccessArticle

From Patterns to Deviations: Detecting Behavioural Drift for Mental Health Monitoring Using Smartphone and Wearable Data

by Eden Tolosa, Isibor Kennedy Ihianle, Pedro Machado, Salisu Wada Yahaya and Ahmad Lotfi

Electronics 2026, 15(4), 885; https://doi.org/10.3390/electronics15040885 - 20 Feb 2026

Viewed by 781

Abstract

Monitoring behavioural drift, a sustained shift in an individual’s daily activity, sleep, or social patterns, offers a significant lens for early mental health intervention. However, detecting these drifts in free-living settings remains challenging due to the absence of ground-truth labels, the temporal complexity [...] Read more.

Monitoring behavioural drift, a sustained shift in an individual’s daily activity, sleep, or social patterns, offers a significant lens for early mental health intervention. However, detecting these drifts in free-living settings remains challenging due to the absence of ground-truth labels, the temporal complexity of human behaviour, and fragmentation across heterogeneous sensing modalities. This paper proposes a multimodal approach to quantify and detect behavioural drift using longitudinal data from over 500 university students in the NetHealth cohort. We extract personalised, longitudinal features spanning three behavioural domains: physical activity, sleep hygiene, and communication diversity and model deviations relative to rolling, individual-specific statistical baselines. To differentiate transient anomalies from meaningful behavioural change, we introduce a sustained streak mechanism that identifies persistent drift episodes. We evaluate three temporal modelling strategies: Isolation Forest, Convolutional Neural Networks, and Long Short-Term Memory networks across both single-modality and fused approaches. Our findings indicate that recurrent models offer the strongest performance, highlighting the necessity of capturing temporal dependencies in behavioural data. Furthermore, we find that cross-modal correlations between drift signals are weak, confirming that activities, sleep, and communication provide complementary, non-redundant insights into an individual’s well-being. This work establishes the methodological basis for integrating multimodal sensing data to monitor mental health trajectories, providing a scalable path towards early intervention in digital health. Full article

(This article belongs to the Section Artificial Intelligence)

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16 pages, 2588 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Smart Home IoT Forensics in Matter Ecosystems: A Data Extraction Method Using Multi-Admin

by Sungbum Kim, Sungmoon Kwon and Taeshik Shon

Electronics 2026, 15(4), 884; https://doi.org/10.3390/electronics15040884 - 20 Feb 2026

Viewed by 728

Abstract

As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data [...] Read more.

As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data extraction procedures that are specific to each device and platform, which increases the technical burden and time costs for investigators. To address these challenges, this study proposes a method that leverages Matter Multi-Admin support for multiple fabrics to enable efficient data acquisition from Matter-enabled IoT devices, regardless of the underlying smart home platform. This method configures a forensic Matter controller using chip-tool and commissions IoT devices that have already been commissioned to a smart home platform into a secondary fabric via Multi-Admin. The forensic controller then performs data extraction using standardized Matter interfaces. The proposed approach was validated on our smart home testbed by targeting a Matter smart bulb commissioned to the SmartThings platform and successfully extracting data generated by the platform, thereby demonstrating the utility of the method. The results indicate that the method enables nondestructive and efficient evidence acquisition from smart home IoT devices and can support future research and real-world investigations. Full article

(This article belongs to the Special Issue New Challenges in IoT Security)

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27 pages, 1628 KB

Open AccessArticle

Synthetic Data Augmentation for Imbalanced Tabular Data: A Comparative Study of Generation Methods

by Dong-Hyun Won, Kwang-Seong Shin and Sungkwan Youm

Electronics 2026, 15(4), 883; https://doi.org/10.3390/electronics15040883 - 20 Feb 2026

Cited by 1 | Viewed by 1775

Abstract

Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. [...] Read more.

Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. We evaluate four augmentation approaches—Synthetic Minority Over-sampling Technique (SMOTE), Gaussian Copula, Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN)—using the University of California Irvine (UCI) Bank Marketing dataset, which exhibits a class imbalance ratio of approximately 7.88:1. Our experimental framework assesses each method across three dimensions: statistical fidelity to the original data distribution evaluated through four complementary metrics (marginal numerical similarity, categorical distribution similarity, correlation structure preservation, and Kolmogorov–Smirnov test), machine learning utility measured through classification performance, and minority class detection capability. Results indicate that all augmentation methods achieved statistically significant improvements over the baseline (

p < 0.05

). SMOTE achieved the highest recall (54.2%, a 117.6% relative improvement over the baseline) and F1-Score (0.437, +22.4% over the baseline) for minority class detection, while Gaussian Copula provided the highest composite fidelity score (0.930) with competitive predictive performance. A weak negative correlation (

ρ = - 0.30

) between composite fidelity and classification performance was observed, suggesting that higher statistical fidelity does not necessarily translate to better downstream task performance. Deep learning-based methods (TVAE, CTGAN) showed statistically significant improvements over the baseline (recall: +58% to +63%) but underperformed compared to simpler methods under default configurations, suggesting the need for larger training samples or more extensive hyperparameter tuning. These findings offer reference points for practitioners working with moderately imbalanced tabular data with limited minority class samples, supporting the selection of generation strategies based on specific requirements regarding data fidelity and classification objectives. Full article

(This article belongs to the Special Issue Data-Related Challenges in Machine Learning: Theory and Application)

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29 pages, 31856 KB

Open AccessFeature PaperArticle

A Vision–Locomotion Framework Toward Obstacle Avoidance for a Bio-Inspired Gecko Robot

by Wenrui Xiang, Barmak Honarvar Shakibaei Asli and Aihong Ji

Electronics 2026, 15(4), 882; https://doi.org/10.3390/electronics15040882 - 20 Feb 2026

Viewed by 623

Abstract

This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a [...] Read more.

This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a flexible spine and multi-jointed limbs, providing a physical basis for adaptive locomotion. For perception, a custom obstacle detection dataset was constructed from the robot’s onboard camera view and used to train a YOLOv5-based detection model. Experimental results show that the trained model achieves a mean average precision (mAP) of 0.979 and a maximum F1-score of 0.97 at an optimal confidence threshold, demonstrating reliable real-time obstacle perception under diverse indoor conditions. For motion control, a central pattern generator (CPG) based on Hopf oscillators is implemented to generate rhythmic locomotion. Experimental evaluations confirm stable diagonal gait generation, with coordinated joint trajectories oscillating at 1 Hz. The flexible spine exhibits periodic lateral deflection with peak amplitudes of ±15°, ±10°, and ±8° across spinal joints, enhancing locomotion continuity and turning capability. Physical robot experiments further demonstrate smooth straight-line crawling enabled by the coupled limb–spine motion. While visual perception and CPG-based locomotion are experimentally validated as independent subsystems, their real-time closed-loop integration is not implemented in this study. Instead, this work establishes a system-level framework and experimental baseline for future perception–motion coupling, providing a foundation for closed-loop obstacle avoidance and autonomous navigation in bio-inspired gecko robots. Full article

(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)

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19 pages, 358 KB

Open AccessArticle

Edge-Level Forest Fire Prediction with Selective Communication in Hierarchical Wireless Sensor Networks

by Ahshanul Haque and Hamdy Soliman

Electronics 2026, 15(4), 881; https://doi.org/10.3390/electronics15040881 - 20 Feb 2026

Cited by 1 | Viewed by 527

Abstract

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy [...] Read more.

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article

(This article belongs to the Special Issue AI and Machine Learning in Recommender Systems and Customer Behavior)

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19 pages, 3986 KB

Open AccessArticle

A Hybrid Prediction-Axiom Dual-Driven Port Selection Algorithm for Fluid Antenna Systems in 6G High-Mobility Scenarios

by Shuo Wang and Hongxing Zheng

Electronics 2026, 15(4), 880; https://doi.org/10.3390/electronics15040880 - 20 Feb 2026

Viewed by 431

Abstract

A significant bottleneck for the practical deployment of fluid antenna systems (FASs) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel “prediction-axiom” dual-driven paradigm is [...] Read more.

A significant bottleneck for the practical deployment of fluid antenna systems (FASs) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel “prediction-axiom” dual-driven paradigm is introduced that fundamentally differs from pure data-driven approaches. The core innovation lies in using an enhanced unified adaptive modeling algorithm (UAMA) not for direct decision-making but as a computational foundation to enable information-theoretic axioms under sparse observation conditions (30% of ports). The UAMA predictor, leveraging spatiotemporal correlations, accurately reconstructs the full channel state from limited measurements. This prediction then empowers an information-theoretic scoring mechanism, which synergizes Fisher information, curvature metrics, and port entropy to transform optimal port selection into a tractable maximization problem. Consequently, the system outage probability remains close to the ideal performance limit achievable under full observability. Tests on diverse antenna systems confirm the algorithm’s high accuracy and robust adaptive capability. This work delivers a reliable, low-cost implementation strategy for 6G dynamic networks, effectively bridging the gap between mathematical theory and practical FAS deployment. Full article

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20 pages, 3082 KB

Open AccessArticle

Impact of Null-Flux Coil Shape on Superconducting Electrodynamic Suspension (EDS) Maglev

by Haochen Shi, Boyang Shen, Zhihao Chen and Lin Fu

Electronics 2026, 15(4), 879; https://doi.org/10.3390/electronics15040879 - 20 Feb 2026

Viewed by 683

Abstract

Superconducting electrodynamic suspension (EDS) maglev technology has strong potential for ultra-high-speed transportation, with advantages such as self-stability and a large suspension gap. The magneto-electric force relationship between the onboard superconducting magnet and figure-eight null-flux coils is the key to improving system performance. This [...] Read more.

Superconducting electrodynamic suspension (EDS) maglev technology has strong potential for ultra-high-speed transportation, with advantages such as self-stability and a large suspension gap. The magneto-electric force relationship between the onboard superconducting magnet and figure-eight null-flux coils is the key to improving system performance. This article shows a novel study on the impact of the shape of null-flux coils on the superconducting EDS maglev system, which has not been systematically studied before. A 3D model of the suspension system of EDS maglev was built using the finite element method (FEM) to study the impact of the null-flux coils’ shape. The electromagnetic forces generated by the system were calculated and compared with those in the literature to validate the model. The results showed that rectangular and circular coils displayed different influences on the components of the electromagnetic force. New results and analysis from the article show that the null-flux coil shape is a promising option for system performance optimization and can provide a theoretical basis for future improvements to the high-speed EDS maglev system. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

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30 pages, 4292 KB

Open AccessReview

Optical Network Security: Threats, Techniques, and Future Directions

by Anna Gazani, Athanasios Mantzavinos, Polyxeni Tsompanoglou, Konstantinos Kantelis, Sophia Petridou, Petros Nicopolitidis and Georgios Papadimitriou

Electronics 2026, 15(4), 878; https://doi.org/10.3390/electronics15040878 - 20 Feb 2026

Viewed by 1533

Abstract

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. [...] Read more.

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. This paper provides a comprehensive survey of vulnerabilities and countermeasures in modern optical networks, spanning the physical, control, and cross-layer dimensions. We analyze major architectures—including WDM, TDM, PON, EON, and IP-over-WDM—and examine how their structural properties shape their security posture. A threat taxonomy is presented covering physical-layer attacks such as fiber tapping, optical jamming, crosstalk exploitation, and signal injection; control-plane risks including spoofing, malicious signaling, and SDN manipulation; and broader cross-layer attack vectors. We review state-of-the-art defense mechanisms, including physical-layer security (PLS), spectrum randomization, chaotic optical coding, device-level authentication, survivability techniques, intelligent monitoring, and quantum-secure solutions such as QKD. By integrating insights from recent experimental and operational studies, the survey highlights emerging challenges and identifies open problems related to secure orchestration, multi-tenant environments, and quantum-era resilience. The objective is to guide researchers, engineers, and network operators toward robust and future-proof security strategies for next-generation optical infrastructures. Full article

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16 pages, 1038 KB

Open AccessFeature PaperEditor’s ChoiceArticle

The Agency-First Framework: Operationalizing Human-Centric Interaction and Evaluation Heuristics for Generative AI

by Christos Troussas, Christos Papakostas, Akrivi Krouska and Cleo Sgouropoulou

Electronics 2026, 15(4), 877; https://doi.org/10.3390/electronics15040877 - 20 Feb 2026

Viewed by 1369

Abstract

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces [...] Read more.

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces the Agency-First Framework (AFF), which combines cognitive engineering and co-active design approaches to formally define human-AI collaboration. This is operationalized through the development of ten Generative AI Agency (GAIA) Heuristics, a systematic method for evaluating agency-centric interactions within stochastic generative settings. By translating the theoretical layers of the AFF into measurable criteria, the GAIA heuristics provide the necessary instrument for the empirical auditing of existing systems and the guidance of agency-centric redesigns. Unlike existing assistive AI guidelines that focus on output-level usability, the AFF establishes agency as a first-class design construct, enabling mid-process intervention and the steering of the model’s latent reasoning trajectory. Validation of the AFF was conducted through a two-tiered empirical evaluation: (1) an expert heuristic audit of state-of-the-art platforms, such as ChatGPT-o1 and Midjourney v6, which achieved high inter-rater reliability, and (2) a controlled redesign study. The latter demonstrated that agency-centric interfaces significantly enhance the Sense of Agency and Intent Alignment Accuracy compared to baseline prompt-response models, even when introducing a deliberate increase in task completion time—a phenomenon we describe as “productive friction” or an intentional interaction slowdown designed to prioritize cognitive engagement and user control over raw speed. Overall, the findings suggest that the restoration of meaningful user agency requires a shift from “seamless” system efficiency towards “productive friction”, where controllability and transparency within the generative process are prioritized. The major contribution of this work is the provision of a scalable, empirically validated framework and set of heuristics that equip designers to move beyond prompt-centric interaction, establishing a methodological foundation for agency-preserving generative AI systems. Full article

(This article belongs to the Special Issue The Role of Artificial Intelligence in Enhancing Human-Computer Interaction)

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