Electronics

31 pages, 897 KB

Open AccessReview

A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications

by Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Can Uzay, Mehmet Serkan Tosun and Oktay Karakuş

Electronics 2025, 14(18), 3580; https://doi.org/10.3390/electronics14183580 (registering DOI) - 9 Sep 2025

Abstract

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish [...] Read more.

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including parameter-efficient fine-tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labor concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of unified multimodal architectures capable of processing varied data inputs and the emergence of agentic systems that exhibit complex behaviors such as tool use and planning. We identify critical open problems, such as detectability, data contamination, generalization, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance. Full article

(This article belongs to the Section Artificial Intelligence)

16 pages, 3433 KB

Open AccessArticle

Incremental Spatio-Temporal Augmented Sampling for Power Grid Operation Behavior Recognition

by Lingwen Meng, Di He, Guobang Ban and Siqi Guo

Electronics 2025, 14(18), 3579; https://doi.org/10.3390/electronics14183579 (registering DOI) - 9 Sep 2025

Abstract

Accurate recognition of power grid operation behaviors is crucial for ensuring both safety and operational efficiency in smart grid systems. However, this task presents significant challenges due to dynamic environmental variations, limited labeled training data availability, and the necessity for continuous model adaptation. [...] Read more.

Accurate recognition of power grid operation behaviors is crucial for ensuring both safety and operational efficiency in smart grid systems. However, this task presents significant challenges due to dynamic environmental variations, limited labeled training data availability, and the necessity for continuous model adaptation. To overcome these limitations, we propose an Incremental Spatio-temporal Augmented Sampling (ISAS) method for power grid operation behavior recognition. Specifically, we design a spatio-temporal Feature-Enhancement Fusion Module (FEFM) which employs multi-scale spatio-temporal augmented fusion combined with a cross-scale aggregation mechanism, enabling robust feature learning that is resilient to environmental interference. Furthermore, we introduce a Selective Replay Mechanism (SRM) that implements a dual-criteria sample selection strategy based on error variability and feature-space divergence metrics, ensuring optimal memory bank updates that simultaneously maximize information gain while minimizing feature redundancy. Experimental results on the power grid behavior dataset demonstrate significant advantages of the proposed method in recognition robustness and knowledge retention compared to other methods. For example, it achieves an accuracy of 89.80% on sunny days and maintains exceptional continual learning stability with merely 2.74% forgetting rate on three meteorological scenarios. Full article

(This article belongs to the Special Issue Applications and Challenges of Image Processing in Smart Environment)

27 pages, 2066 KB

Open AccessArticle

Enhancing Multiple-Access Capacity and Synchronization in Satellite Beam Hopping with NOMA-SIC

by Tengfei Hui, Shenghua Zhai, Mingming Hui, Fengkui Gong, Ruyan Lin and Yulong Fu

Electronics 2025, 14(18), 3578; https://doi.org/10.3390/electronics14183578 (registering DOI) - 9 Sep 2025

Abstract

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal [...] Read more.

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal Multiple Access (NOMA) with MF-TDMA, employing Successive Interference Cancelation (SIC) for multi-user signal separation. A bi-directional adaptive carrier synchronization method and optimized burst structure are introduced, which collectively reduce synchronization overhead by over 40% compared to MF-TDMA. Simulations demonstrate a dramatically improved frame error rate of 0.0005% at 4 dB SNR—30 times lower than the 0.016% achieved by MF-TDMA—and a transmission efficiency of 92–97%, significantly outperforming conventional MF-TDMA. These results validate the proposed method’s substantial gains in capacity and efficiency for next-generation satellite systems. Full article

(This article belongs to the Special Issue Advances in Security Enforcement for Intelligent Wireless Communication)

21 pages, 10416 KB

Open AccessArticle

YOLOv11-Based UAV Foreign Object Detection for Power Transmission Lines

by Depeng Gao, Yihan Yin, Han Zhang, Changping Li and Bingshu Wang

Electronics 2025, 14(18), 3577; https://doi.org/10.3390/electronics14183577 (registering DOI) - 9 Sep 2025

Abstract

Foreign object detection on transmission lines poses a significant threat to power grid security, while conventional manual inspection methods are inefficient and pose safety risks. To overcome the challenges of detecting foreign objects in complex environments, this paper proposes an enhanced YOLOv11_SDI detection [...] Read more.

Foreign object detection on transmission lines poses a significant threat to power grid security, while conventional manual inspection methods are inefficient and pose safety risks. To overcome the challenges of detecting foreign objects in complex environments, this paper proposes an enhanced YOLOv11_SDI detection framework with two key contributions. Firstly, a novel hierarchical Spatial-channel Dynamic Inference (SDI) module is integrated into YOLOv11, employing an adaptive feature fusion mechanism to enhance multi-scale representation. Secondly, a lightweight spatial attention unit is introduced to improve region-of-interest localization without compromising computational efficiency. In addition, the publicly available FOTL_Drone dataset is expanded to 5980 UAV images through systematic data augmentation, covering six critical foreign object categories. Comprehensive experiments validate the model’s superior performance, achieving state-of-the-art 95.2% mAP@0.50 with only 3.74 M parameters, demonstrating its potential for practical transmission line inspection applications. Full article

23 pages, 15956 KB

Open AccessFeature PaperArticle

A Photovoltaic Light Sensor-Based Self-Powered Real-Time Hover Gesture Recognition System for Smart Home Control

by Nora Almania, Sarah Alhouli and Deepak Sahoo

Electronics 2025, 14(18), 3576; https://doi.org/10.3390/electronics14183576 (registering DOI) - 9 Sep 2025

Abstract

Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light [...] Read more.

Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light sensors are self-powered, battery-free, flexible, portable, and easily deployable on various surfaces throughout the home. They enable natural, intuitive, hover-based interaction, which could create a positive user experience. In this paper, we present the development and evaluation of a real-time, hover gesture recognition system that can control multiple smart home devices via a self-powered photovoltaic interface. Five popular supervised machine learning algorithms were evaluated using gesture data from 48 participants. The random forest classifier achieved high accuracies. However, a one-size-fits-all model performed poorly in real-time testing. User-specific random forest models performed well with 10 participants, showing no significant difference in offline and real-time performance and under normal indoor lighting conditions. This paper demonstrates the technical feasibility of using photovoltaic surfaces as self-powered interfaces for gestural interaction systems that are perceived to be useful and easy to use. It establishes a foundation for future work in hover-based interaction and sustainable sensing, enabling human–computer interaction researchers to explore further applications. Full article

(This article belongs to the Special Issue Human-Computer Interaction in Intelligent Systems, 2nd Edition)

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23 pages, 998 KB

Open AccessArticle

A Two-Stage Algorithm for the Design of Wide-Area Damping Controllers

by Henrique Resende de Almeida and Murilo E. C. Bento

Electronics 2025, 14(18), 3575; https://doi.org/10.3390/electronics14183575 (registering DOI) - 9 Sep 2025

Abstract

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and [...] Read more.

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and inter-area modes and required new damping control strategies for these modes. The expansion of the use of Phasor Measurement Units in power systems has led to the development of new control strategies such as Wide-Area Damping Controllers (WADCs) that use data from PMUs to dampen low-frequency oscillations. Although the benefits of WADCs are promising, there are challenges in designing a WADC. This paper proposes a two-stage algorithm for the robust design of a WADC for modern power systems. The first stage consists of solving an optimization model and finding the WADC parameters that maximize the damping ratios of all modes of the linearized system model for a set of operating points. The second stage consists of refining the WADC parameters through an iterative algorithm. Cases were studied for a set of IEEE 68-bus operating points through modal analysis and time-domain simulations. The results obtained demonstrated the good performance of the proposed two-stage algorithm compared with an existing WADC design method based on a Linear Quadratic Regulator. Full article

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18 pages, 330 KB

Open AccessArticle

Design and Validation of SystemVerilog I²C VIP with Integrated Assertions and Error Injection Strategies

by Chien-Yu Lu, Wei-Zhen Su, Cheng-Hao Deng and Yu-Cheng Liao

Electronics 2025, 14(18), 3574; https://doi.org/10.3390/electronics14183574 (registering DOI) - 9 Sep 2025

Abstract

In this paper, we report the design and verification methodology of a SystemVerilog-based I²C protocol Verification IP (VIP) based not only on assertion-based verification but also on the new checkout error injection techniques. The resulting VIP is designed as a set [...] Read more.

In this paper, we report the design and verification methodology of a SystemVerilog-based I²C protocol Verification IP (VIP) based not only on assertion-based verification but also on the new checkout error injection techniques. The resulting VIP is designed as a set of loose-coupled modules for protocol description, transaction generation, and automatic protocol checking with SystemVerilog Assertions (SVAs). Timing, multi-master arbitration, and error recovery related to I²C protocol verification challenges are achieved using embedded assertion monitors and focused error injection scenarios on the testbench. The paper describes the inclusion of assertion-based monitors used for checking real-time protocol compliance as well as the ability for systematic error injection to expose corner-case bugs and verify the strength of the DUT and the verification environment. We present our experimental results that demonstrate the effectiveness of proposed strategies on coverage, bug leakage, and reduction in debug cycles. The approach also provides a useful guideline for verification engineers who need to build protocol VIPs or wish to improve the efficiency of their verification flow with assertion-led methods. Full article

18 pages, 3524 KB

Open AccessArticle

Efficient Multi-Topology Failure Tolerance Mechanism in Polymorphic Network

by Ziyong Li, Bai Lin, Wenyu Jiang and Le Tian

Electronics 2025, 14(18), 3573; https://doi.org/10.3390/electronics14183573 (registering DOI) - 9 Sep 2025

Abstract

Enhancing the failure tolerance ability of networks is crucial, as node or link failures are common occurrences on-site. The current fault tolerance schemes are divided into reactive and proactive schemes. The reactive scheme requires detection and repair after the failure occurs, which may [...] Read more.

Enhancing the failure tolerance ability of networks is crucial, as node or link failures are common occurrences on-site. The current fault tolerance schemes are divided into reactive and proactive schemes. The reactive scheme requires detection and repair after the failure occurs, which may lead to long-term network interruptions. The proactive scheme can reduce recovery time through preset backup paths, but requires additional resources. Aiming at the problems of long recovery time or high overhead of the current failure tolerance schemes, the Polymorphic Network adopts field-definable network baseline technology, which can support diversified addressing and routing capabilities, making it possible to implement a more complex and efficient failure tolerance scheme. Inspired by this, we propose an efficient Multi-topology Failure Tolerance mechanism in Polymorphic Network (MFT-PN). The MFT-PN embeds a failure recovery function into the packet processing logic by leveraging the full programmable characteristics of the network element, improving failure recovery efficiency. The backup path information is pushed into the header of the failed packet to reduce the flow table storage overhead. Meanwhile, MFT-PN introduces the concept of multi-topology routing by constructing multiple logical topologies, with each topology adopting different failure recovery strategies. Then, we design a multi-topology loop-free link backup algorithm to calculate the backup path for each topology, providing extensive coverage for different failure scenarios. Experimental results show that compared with the existing strategies, MFT-PN can reduce resource overhead by over 72% and the packet loss rate by over 59%, as well as effectively cope with multiple failure scenarios. Full article

(This article belongs to the Section Networks)

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24 pages, 1985 KB

Open AccessArticle

Mining Causal Chains for Tower Crane Accidents Using an Improved Transformer and Complex Network Model

by Qian Wang, Lifeng Zhao, Jiahao Lei, Kangxin Li, Jie Chen, Giorgio Monti, Yandi Ai and Zhi Li

Electronics 2025, 14(18), 3572; https://doi.org/10.3390/electronics14183572 (registering DOI) - 9 Sep 2025

Abstract

Tower crane structural failures remain a major safety concern on construction sites. To improve accident prevention, this study proposes an intelligent framework that combines an improved Transformer model with a Directional Interest Score (DIS) Apriori algorithm and complex-network analysis. A corpus of 535 [...] Read more.

Tower crane structural failures remain a major safety concern on construction sites. To improve accident prevention, this study proposes an intelligent framework that combines an improved Transformer model with a Directional Interest Score (DIS) Apriori algorithm and complex-network analysis. A corpus of 535 tower crane accident reports (2002–2024) was compiled and annotated with causal and accident entities according to system–safety theory. Segment embeddings were introduced to the Transformer to reinforce boundary detection, enabling accurate extraction of causative factors and relation triples. The DIS-Apriori algorithm was then used to mine both positive and negative association rules while aggressively pruning irrelevant item sets. Eventually, causative factors were mapped into a weighted, directed complex network where edge weights reflect the absolute frequency difference between positive and negative rules, and edge directions correspond to their signs. Experiments show that the Transformer achieves higher precision and recall than baseline models, and DIS-Apriori substantially reduces unnecessary item-set complexity while preserving critical rules. Network analysis revealed five critical causal links and a closed-loop causal link that warrant priority intervention. The proposed method delivers a data-driven, explainable tool for pinpointing key risk sources and designing targeted mitigation strategies, offering practical value for intelligent safety management of tower cranes. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications)

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17 pages, 1806 KB

Open AccessArticle

Research on Dynamic Weighted Coupling Model of Multi-Energy System Driven by Meteorological Risk Perception

by Yunjie Zhang, Xinyu Yin, Wenxi Li, Gang Xu and Yi Wang

Electronics 2025, 14(18), 3571; https://doi.org/10.3390/electronics14183571 (registering DOI) - 9 Sep 2025

Abstract

With the aggravation of global climate change and the increasing frequency and intensity of extreme weather events, power systems with a high proportion of renewable energy are under threat. In response, in traditional wind–solar–storage–hydrogen multi-energy systems, it is difficult to balance power supply [...] Read more.

With the aggravation of global climate change and the increasing frequency and intensity of extreme weather events, power systems with a high proportion of renewable energy are under threat. In response, in traditional wind–solar–storage–hydrogen multi-energy systems, it is difficult to balance power supply resilience, economy, and environmental protection, and such systems cannot meet actual demand due to the lack of a dynamic meteorological integration mechanism. Therefore, a dynamic collaborative optimization model of a multi-energy system driven by meteorological risk perception is proposed. The dynamic meteorological risk factor integrating various meteorological elements is introduced, and the risk response mechanism is established based on the system’s energy storage state to realize the adaptive adjustment of coupled weight parameters and achieve the goal of collaborative optimization of power supply resilience, economy, and environmental protection. The case analysis results show that, compared with other models, the proposed model can reduce the power supply shortage by 23.1% in extreme weather periods, and the system’s survival probability can reach 97.1% at most. The proposed model minimizes the assembly while ensuring that carbon emissions meet standards, and achieves the collaborative optimization of power supply toughness, economy, and environmental protection. It provides a theoretical tool for solving the collaborative optimization problem that energy systems with a high proportion of renewables face in coping with climate risks. Full article

(This article belongs to the Section Systems & Control Engineering)

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17 pages, 1335 KB

Open AccessArticle

User Authentication Using Graph Neural Networks (GNNs) for Adapting to Dynamic and Evolving User Patterns

by Hyun-Sik Choi

Electronics 2025, 14(18), 3570; https://doi.org/10.3390/electronics14183570 (registering DOI) - 9 Sep 2025

Abstract

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more [...] Read more.

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more accurate and efficient user authentication methods. One such approach involves the use of biometric signals to enhance security. However, biometric methods face significant challenges in ensuring stable authentication accuracy, primarily due to variations in the user’s environment, physical activity, and health conditions. To address these issues, this paper proposes a biometric-signal-based user authentication system using graph neural networks (GNNs). The feasibility of the proposed system was evaluated using an electromyogram (EMG) dataset specifically constructed by Chosun University for user authentication research. GNNs have demonstrated exceptional performance in modeling the relationships among complex data and attracted attention in various fields. Specifically, GNNs are well-suited for modeling user behavioral patterns while considering temporal and spatial relationships, making them an ideal method for adapting to dynamic and evolving user patterns. Unlike traditional neural networks, GNNs can dynamically learn and adapt to changes or evolutions in user behavioral patterns over time. This paper describes the design and implementation of a user authentication system using GNNs with an EMG dataset and discusses how the system can adapt to dynamic and changing user patterns. Full article

(This article belongs to the Section Artificial Intelligence)

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20 pages, 2553 KB

Open AccessArticle

CCIBA: A Chromatic Channel-Based Implicit Backdoor Attack on Deep Neural Networks

by Chaoliang Li, Jiyan Liu, Yang Liu and Shengjie Yang

Electronics 2025, 14(18), 3569; https://doi.org/10.3390/electronics14183569 (registering DOI) - 9 Sep 2025

Abstract

Deep neural networks (DNNs) excel in image classification but are vulnerable to backdoor attacks due to reliance on external training data, where specific markers trigger preset misclassifications. Existing attack techniques have an obvious trade-off between the effectiveness of the triggers and the stealthiness, [...] Read more.

Deep neural networks (DNNs) excel in image classification but are vulnerable to backdoor attacks due to reliance on external training data, where specific markers trigger preset misclassifications. Existing attack techniques have an obvious trade-off between the effectiveness of the triggers and the stealthiness, which limits their practical application. For this purpose, in this paper, we develop a method—chromatic channel-based implicit backdoor attack (CCIBA), which combines a discrete wavelet transform (DWT) and singular value decomposition (SVD) to embed triggers in the frequency domain through the chromaticity properties of the YUV color space. Experimental validation on different image datasets shows that compared to existing methods, CCIBA can achieve a higher attack success rate without a large impact on the normal classification ability of the model, and its good stealthiness is verified by manual detection as well as different experimental metrics. It successfully circumvents existing defense methods in terms of sustainability. Overall, CCIBA strikes a balance between covertness, effectiveness, robustness and sustainability. Full article

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Electronics, Volume 14, Issue 18 (September-2 2025) – 12 articles

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