Next Issue
Volume 14, April-2
Previous Issue
Volume 14, March-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Volume 14
Issue 7
share announcement

Electronics, Volume 14, Issue 7 (April-1 2025) – 249 articles

Cover Story (view full-size image): How can we ensure that AI in medicine and education respects human values? In the following paper, we introduce the Ethical Firewall Architecture, a pioneering solution that embeds provable ethical constraints into AI decision-making. Discover how it combines cutting-edge technology, including blockchain immutability, with human oversight to address ethical challenges and build necessary trust in AI applications. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
17 pages, 9409 KiB  
Article
Dynamic Client Selection and Group-Balanced Personalization for Data-Imbalanced Federated Speech Recognition
by Chundong Xu, Ziyu Wu, Fengpei Ge and Yuheng Zhi
Electronics 2025, 14(7), 1485; https://doi.org/10.3390/electronics14071485 - 7 Apr 2025
Viewed by 335
Abstract
Federated learning has been widely applied in automatic speech recognition. However, variations in speaker behaviors result in a significant data imbalance across client devices. Conventional federated speech recognition algorithms typically use fixed probabilities to select clients for each round in model training, often [...] Read more.
Federated learning has been widely applied in automatic speech recognition. However, variations in speaker behaviors result in a significant data imbalance across client devices. Conventional federated speech recognition algorithms typically use fixed probabilities to select clients for each round in model training, often overlooking the disparities in data volume among clients. In reality, the substantial differences in data quantity can extend the training duration and compromise the stability of the global model. Moreover, models trained through federated learning on global data often fail to achieve optimal performance for individual local clients. While personalized federated learning strategies hold promise for enhancing model performance, the inherent diversity of speech data makes it challenging to apply state-of-the-art personalized methods effectively to speech recognition tasks. In this paper, a dynamic client selection algorithm is proposed to solve the problem of data disparities among different clients. It can be effectively combined with most federated learning algorithms and dynamically adjusts the selection probabilities of clients based on their dataset size during training. Experimental results demonstrate that this algorithm saved training time by 26% compared to traditional methods on public datasets while maintaining the equivalent model performance. To optimize the personalized federated learning, this paper proposes a novel group-balanced personalization strategy that fine-tunes groupings of clients based on their dataset size. The experimental results show that this algorithm brought a relatively 12% reduction in character error rate, while it did not increase computational costs. In particular, the group-balanced personalization effectively improved the model performance for clients with smaller datasets than local fine-tuning. The combination of dynamic client selection and group-balanced personalization significantly enhanced training efficiency and model performance. Full article
►▼ Show Figures

Figure 1

22 pages, 19106 KiB  
Article
Enhanced Charge Pump Architecture with Feedback Supply Selector for Optimized Switching Performance
by Cristian Stancu, Anca Andreea Mitu, Teodora Ionescu, Andrei Neacsu, Lidia Dobrescu and Dragos Dobrescu
Electronics 2025, 14(7), 1484; https://doi.org/10.3390/electronics14071484 - 7 Apr 2025
Viewed by 331
Abstract
Conventional operational amplifier designs often experience parameter performance issues during the transition between complementary input differential stages, which restricts the full rail-to-rail common mode voltage swing. This paper presents an innovative charge pump architecture featuring a feedback supply selector that optimizes the transition [...] Read more.
Conventional operational amplifier designs often experience parameter performance issues during the transition between complementary input differential stages, which restricts the full rail-to-rail common mode voltage swing. This paper presents an innovative charge pump architecture featuring a feedback supply selector that optimizes the transition performance. The proposed approach employs a switched-capacitor technique to boost the supply voltage by 1.5 V relative to the input voltage, thereby enabling the use of a single pMOS differential input stage. The novel supply selector dynamically chooses the maximum available voltage between the external supply and the boosted output, ensuring efficient transistor switching and improved biasing. Schematic-level and post-layout simulations in a 250 nm CMOS process validate the design under varied load currents, supply voltages, temperatures, and process corners. Results show a significant reduction in output voltage ripple, with a maximum value of 48 mV achieved post-layout, and enhanced overall efficiency, even under higher load currents. This architecture provides a robust and scalable solution for advanced operational amplifiers, particularly in fields where high performance and stability are critical. Full article
(This article belongs to the Special Issue CMOS Integrated Circuits Design)
►▼ Show Figures

Figure 1

31 pages, 1050 KiB  
Article
Formal Verification of Autonomous Vehicle Group Control Systems via Specification Translation of Multitask Hybrid Observational Transition Systems
by Yifan Wang, Masaki Nakamura, Ryo Takano, Takuya Matsumoto and Kazutoshi Sakakibara
Electronics 2025, 14(7), 1483; https://doi.org/10.3390/electronics14071483 - 7 Apr 2025
Viewed by 172
Abstract
In multitasking systems, concurrent execution leads to an exponential growth of the state space, posing significant challenges for formal verification. This complexity is further exacerbated in hybrid systems that integrate discrete and continuous dynamics. To address these challenges, we propose to model multitasking [...] Read more.
In multitasking systems, concurrent execution leads to an exponential growth of the state space, posing significant challenges for formal verification. This complexity is further exacerbated in hybrid systems that integrate discrete and continuous dynamics. To address these challenges, we propose to model multitasking hybrid systems and systematically verify system properties through formal verification methods, using synergistic formal verification of model checking and theorem proving to ensure rigorous correctness analysis. We, therefore, introduce a transformation framework that converts behavioral specifications into rewrite specifications, enabling the integration of verification techniques from both approaches. To demonstrate the effectiveness of our approach, we model self-driving car group control systems as Multitask Hybrid Observational Transition Systems (MHOTS), a framework extending Observational Transition Systems (OTS) to support hybrid and multitask behaviors, specify their safety properties in theorem proving via CafeOBJ, in model checking via real-time Maude, and verify that the system remains safe regardless of the number of processes involved. The approach leverages specification translation between CafeOBJ and Real-Time Maude to exploit the complementary strengths of theorem proving and model checking. CafeOBJ is an algebraic specification language that provides a rigorous mathematical foundation for theorem proving, enabling the verification of system properties through logical deductions, while Real-Time Maude facilitates model checking, thereby enhancing the safety and reliability of autonomous vehicle systems. This methodology not only confirms the safety properties of the control system but also establishes a robust framework for the future development and validation of autonomous driving technologies. The integration of these formal verification techniques provides a rigorous and systematic approach to ensuring the desired properties of Multitask Hybrid Observational Transition systems, contributing to the advancement of safe and reliable autonomous driving solutions. Full article
►▼ Show Figures

Figure 1

21 pages, 3586 KiB  
Article
CNDD-Net: A Lightweight Attention-Based Convolutional Neural Network for Classifying Corn Nutritional Deficiencies and Leaf Diseases
by Suresh Timilsina, Sandhya Sharma, Samir Gnawali, Kazuhiko Sato, Yoshifumi Okada, Shinya Watanabe and Satoshi Kondo
Electronics 2025, 14(7), 1482; https://doi.org/10.3390/electronics14071482 - 7 Apr 2025
Viewed by 793
Abstract
Plant diseases and nutrient deficiencies pose significant challenges to food production, making it crucial to identify them accurately and quickly, as their symptoms can often be similar. Prompt and precise detection is essential to implement effective measures that prevent crop losses. While computer [...] Read more.
Plant diseases and nutrient deficiencies pose significant challenges to food production, making it crucial to identify them accurately and quickly, as their symptoms can often be similar. Prompt and precise detection is essential to implement effective measures that prevent crop losses. While computer vision techniques have demonstrated effectiveness in classification, their high computational demands have limited their adoption by farmers in the field. In this study, a Corn leaf Nutrition Deficiency and Disease network (CNDD-net) is designed based on the ResNet framework, incorporating a depth-wise separable convolution and a convolutional block attention module for a lightweight, high-performance model. The models underwent training, validation, and testing using a corn leaf nutrition deficiencies and diseases data set with seven classes implementing five-fold cross-validation. The performance of the models is assessed using average accuracy, GFLOPs, number of parameters, and model size. Following experiments involving the manipulation of the position of the attention module, the number of feature maps, and the depth of the network, the model was finalised. The CNDD-net design has a model size of 0.24 MB with 48,041 parameters and a GFLOPs of 0.18, providing an average accuracy of 96.71%. Compared to conventional models, this research demonstrates optimal performance and computational complexity, offering an efficient, lightweight solution to identify nutritional deficiencies and diseases of corn leaf, thus supporting sustainable agriculture. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image Analysis for Biological Systems)
►▼ Show Figures

Graphical abstract

26 pages, 938 KiB  
Article
Enhancing Personalised Learning with a Context-Aware Intelligent Question-Answering System and Automated Frequently Asked Question Generation
by Eleonora Bernasconi, Domenico Redavid and Stefano Ferilli
Electronics 2025, 14(7), 1481; https://doi.org/10.3390/electronics14071481 - 7 Apr 2025
Viewed by 308
Abstract
The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of [...] Read more.
The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of student questions. This research focuses on developing a context-aware IQAS that leverages advanced Natural Language Processing (NLP) techniques and contextual information, including student learning history and educational content, to provide personalised support. This study also introduces a software tool that utilizes NLP techniques to automatically generate FAQs from educational materials. Employing a hybrid approach combining rule-based and machine learning techniques, the IQAS demonstrated high accuracy in interpreting and responding to a wide range of student queries. The software tool effectively automated the generation of FAQs, creating a valuable resource for personalised learning. The findings suggest that these tools can significantly improve student engagement, motivation, and learning outcomes, highlighting the potential of AI to transform education and pave the way for more personalised, adaptive, and effective learning environments. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing and Their Applications)
►▼ Show Figures

Figure 1

12 pages, 9526 KiB  
Article
Design of Omnidirectional Antennas Using TM22 and Quasi-TM11 Modes with Characteristic Mode Analysis
by Wei Hu, Tao Tang, Liangfu Peng, Maged A. Aldhaeebi, Thamer S. Almoneef and Dongming Tang
Electronics 2025, 14(7), 1480; https://doi.org/10.3390/electronics14071480 - 7 Apr 2025
Viewed by 255
Abstract
This study presents the design of two high-gain omnidirectional antennas with minimal pattern ripple. Antenna I is based on a conventional microstrip patch structure, while Antenna II integrates a modified design with four metal probes. Characteristic mode theory (CMT) was applied to analyze [...] Read more.
This study presents the design of two high-gain omnidirectional antennas with minimal pattern ripple. Antenna I is based on a conventional microstrip patch structure, while Antenna II integrates a modified design with four metal probes. Characteristic mode theory (CMT) was applied to analyze the far-field radiation patterns of both antennas, with a focus on the distinct radiation modes. The analysis revealed that Antenna I operates in the TM22 mode and Antenna II in the quasi-TM11 mode, both exhibiting omnidirectional radiation characteristics. A comparative investigation of four different feeding techniques was conducted to ensure equal amplitude and phase excitation at each port, resulting in a low pattern ripple for both designs. A 1:4 power divider was implemented to validate the designs, and the performance of Antennas I and II was experimentally assessed. The measurement results showed that the −10 dB operating bandwidths of Antennas I and II spanned 2.42–2.50 GHz and 2.34–2.57 GHz, respectively, with corresponding peak gains of 8.0 dBi and 4.55 dBi at a frequency of 2.45 GHz. Full article
►▼ Show Figures

Figure 1

17 pages, 467 KiB  
Article
Data Throughput-Oriented Site Selection: Integrated Downlink Scheduling with Elastic Laser Communication Terminal Deployment
by Pei Lyu, Kanglian Zhao and Hangsheng Zhao
Electronics 2025, 14(7), 1479; https://doi.org/10.3390/electronics14071479 - 7 Apr 2025
Viewed by 235
Abstract
Space-to-ground laser communication (SGLC) offers a paradigm-shifting solution to overcome the bandwidth constraints of radio frequency systems by leveraging laser beams for ultra-high data throughput, although its link availability probability is significantly affected by atmospheric conditions such as cloud cover. Existing ground station [...] Read more.
Space-to-ground laser communication (SGLC) offers a paradigm-shifting solution to overcome the bandwidth constraints of radio frequency systems by leveraging laser beams for ultra-high data throughput, although its link availability probability is significantly affected by atmospheric conditions such as cloud cover. Existing ground station (GS) placement methods decouple site selection from downlink scheduling, failing to effectively quantify the data throughput of candidate sites. This study proposes a data throughput-driven joint optimization framework that integrates downlink scheduling into the site selection model for the first time. Additionally, the site selection model also incorporates equipment cost constraints and service capacity limitations by introducing an integer variable Q to characterize the deployment scale of laser communication terminals (LCTs) at each GS. Through auxiliary variable linearization techniques, the site selection problem is transformed into a tractable integer linear programming (ILP) formulation. A branch-and-bound algorithm is proposed to achieve global optimal solution search. Numerical results demonstrate that the proposed approach improves data throughput compared to the existing method. Full article
►▼ Show Figures

Figure 1

20 pages, 2353 KiB  
Article
ARLO: Augmented Reality Localization Optimization for Real-Time Pose Estimation and Human–Computer Interaction
by Meng Xu, Qiqi Shu, Zhao Huang, Guang Chen and Stefan Poslad
Electronics 2025, 14(7), 1478; https://doi.org/10.3390/electronics14071478 - 7 Apr 2025
Viewed by 281
Abstract
Accurate and real-time outdoor localization and pose estimation are critical for various applications, including navigation, robotics, and augmented reality. Apple’s ARKit, a leading AR platform, employs visual–inertial odometry (VIO) and simultaneous localization and mapping (SLAM) algorithms to enable localization and pose estimation. However, [...] Read more.
Accurate and real-time outdoor localization and pose estimation are critical for various applications, including navigation, robotics, and augmented reality. Apple’s ARKit, a leading AR platform, employs visual–inertial odometry (VIO) and simultaneous localization and mapping (SLAM) algorithms to enable localization and pose estimation. However, ARKit-based systems face positional bias when the device’s camera is obscured, a frequent issue in dynamic or crowded environments. This paper presents a novel approach to mitigate this limitation by integrating position bias correction, context-aware localization, and human–computer interaction techniques into a cohesive interactive module group. The proposed system includes a navigation module, a positioning module, and a front-end rendering module that collaboratively optimize ARKit’s localization accuracy. Comprehensive evaluation across a variety of outdoor environments demonstrates the approach’s effectiveness in improving localization precision. This work contributes to enhancing ARKit-based systems, particularly in scenarios with limited visual input, thereby improving user experience and expanding the potential for outdoor localization applications. Experimental evaluations show that our method improves localization accuracy by up to 92.9% and reduces average positional error by more than 85% compared with baseline ARKit in occluded or crowded outdoor environments. Full article
(This article belongs to the Section Computer Science & Engineering)
►▼ Show Figures

Figure 1

29 pages, 6481 KiB  
Article
MDFFN: Multi-Scale Dual-Aggregated Feature Fusion Network for Hyperspectral Image Classification
by Ge Song, Xiaoqi Luo, Yuqiao Deng, Fei Zhao, Xiaofei Yang, Jiaxin Chen and Jinjie Chen
Electronics 2025, 14(7), 1477; https://doi.org/10.3390/electronics14071477 - 7 Apr 2025
Viewed by 283
Abstract
Employing the multi-scale strategy in hyperspectral image (HSI) classification enables the exploration of complex land-cover structures with diverse shapes. However, existing multi-scale methods still have limitations for fine feature extraction and deep feature fusion, which hinder the further improvement of classification performance. In [...] Read more.
Employing the multi-scale strategy in hyperspectral image (HSI) classification enables the exploration of complex land-cover structures with diverse shapes. However, existing multi-scale methods still have limitations for fine feature extraction and deep feature fusion, which hinder the further improvement of classification performance. In this paper, we propose a multi-scale dual-aggregated feature fusion network (MDFFN) for both balanced and imbalanced environments. The network comprises two main core modules: a multi-scale convolutional information embedding (MCIE) module and a dual aggregated cross-attention (DACA) module. The proposed MCIE module introduces a multi-scale pooling operation to aggregate local features, which efficiently highlights discriminative spectral–spatial information and especially learns key features in small target samples in the imbalanced environment. Furthermore, the proposed DACA module employs a cross-scale interaction strategy to realize the deep fusion of multi-scale features and designs a dual aggregation mechanism to mitigate the loss of information, which facilitates further spatial–spectral feature enhancement. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods on three classical HSI datasets, proving the superiority of the proposed MDFFN. Full article
(This article belongs to the Section Artificial Intelligence)
►▼ Show Figures

Figure 1

19 pages, 2394 KiB  
Article
Quantitative Methodology for Assessing the Quality of Direct Laser Processing of 316L Steel Powder Using Type I and Type II Control Errors
by Oleksandr Vasilevskyi, Alexandra Woods, Matthew Jones and Michael Cullinan
Electronics 2025, 14(7), 1476; https://doi.org/10.3390/electronics14071476 - 7 Apr 2025
Viewed by 624
Abstract
The paper proposes a methodology for assessing the quality of the direct laser melting process of 316L steel powder, which was tested when creating products in a construction furnace of the EOSINT M280 system at different laser powers. The methodology for evaluating the [...] Read more.
The paper proposes a methodology for assessing the quality of the direct laser melting process of 316L steel powder, which was tested when creating products in a construction furnace of the EOSINT M280 system at different laser powers. The methodology for evaluating the quality of the laser melting process is based on measuring the melting temperature of 316L steel powder using an infrared camera, assessing the expanded uncertainty of temperature measurements, and calculating the probabilities of the temperature falling within the established confidence limits based on type I and type II control errors (risks). The experimental investigations revealed that the melting temperature of 316L steel powder was achieved at a laser power of 195 W, with an average value of 1446 °C. It was also found that the maximum expanded measurement uncertainty for the temperature was 7%. In this case, an identification of quality indicators of the laser melting process is proposed, which has three levels: good quality (A), satisfactory quality (B), and unsatisfactory/unacceptable quality (C). The studies showed that the probability of achieving a good/high-quality (A) resulted in the laser melting process of 316L steel powder at a laser power of 195 W was 91%, while the probability of achieving satisfactory quality (B) was 0.03%. These findings contribute to enhancing in situ process monitoring in additive manufacturing, enabling the detection of deviations and adjustments to ensure consistently good quality. The proposed methodology provides a robust framework applicable across different LP-BF/M systems, improving process reliability and reproducibility in industrial and scientific applications. Full article
(This article belongs to the Special Issue New Advance in Stretchable Electronics and Additive Manufacturing)
►▼ Show Figures

Figure 1

1 pages, 128 KiB  
Retraction
RETRACTED: Zhang et al. Effects of Current Filaments on IGBT Avalanche Robustness: A Simulation Study. Electronics 2024, 13, 2347
by Jingping Zhang, Houcai Luo, Huan Wu, Bofeng Zheng, Xianping Chen, Guoqi Zhang, Paddy French and Shaogang Wang
Electronics 2025, 14(7), 1475; https://doi.org/10.3390/electronics14071475 - 7 Apr 2025
Viewed by 168
Abstract
The Electronics Editorial Office retracts the article “Effects of Current Filaments on IGBT Avalanche Robustness: A Simulation Study” [...] Full article
22 pages, 2826 KiB  
Article
Research on Target Detection Algorithm for Complex Traffic Scenes Based on ADVI-CFAR
by Feng Tian, Tianyu Wei, Weibo Fu and Siyuan Wang
Electronics 2025, 14(7), 1474; https://doi.org/10.3390/electronics14071474 - 6 Apr 2025
Viewed by 284
Abstract
To address the issue of reduced target detection accuracy due to interfering targets and clutter reference cells in complex traffic scenarios, we propose the ADVI-CFAR (Adaptive Discriminant Variation Index Constant False Alarm Rate) detection algorithm. Considering that the non-uniformity of the background environment [...] Read more.
To address the issue of reduced target detection accuracy due to interfering targets and clutter reference cells in complex traffic scenarios, we propose the ADVI-CFAR (Adaptive Discriminant Variation Index Constant False Alarm Rate) detection algorithm. Considering that the non-uniformity of the background environment leads to significant variations in signal power magnitude, we introduce a background power transition point to evaluate the uniformity of the background environment within the reference window. Moreover, in complex background environments, clutter distributions often exhibit skewness rather than a Gaussian distribution. We incorporate the higher-order statistical skewness of the clutter to calculate the background power threshold index, thereby improving the accuracy of background power estimation. Then, based on the transition points and clutter power index, the background environment is classified, and an appropriate detection threshold calculation method is chosen for target detection. We conduct a simulation analysis in uniform, non-uniform, and clutter edge environments, and the results show that the identification accuracy exceeds 95% for all three background environments. At a detection probability of 50%, the performance loss is 0.08 dB in uniform environments and 0.36 dB in multi-target environments. When the false alarm probability is set to 10−4, the ADVI-CFAR algorithm significantly suppresses false alarms, with the false alarm peak occurring at 10−3.52. Real data from urban traffic scenarios validate the method, showing that it achieves a high detection accuracy for target detection in real traffic scenarios and effectively meets the radar target detection requirements in practical traffic environments. Full article
►▼ Show Figures

Figure 1

21 pages, 13198 KiB  
Article
Infrared Bionic Compound-Eye Camera: Long-Distance Measurement Simulation and Verification
by Xiaoyu Wang, Linhan Li, Jie Liu, Zhen Huang, Yuhan Li, Huicong Wang, Yimin Zhang, Yang Yu, Xiupeng Yuan, Liya Qiu and Sili Gao
Electronics 2025, 14(7), 1473; https://doi.org/10.3390/electronics14071473 - 6 Apr 2025
Viewed by 224
Abstract
To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is [...] Read more.
To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is experimentally evaluated. The compound-eye camera consists of a small-lens array with a spherical shell, a relay optical system, and a cooling-type mid-wave infrared detector. Based on the spatial arrangement of the small-lens array, a precise simulation imaging model for the compound-eye camera is developed, constructing a virtual imaging space. Distance estimation and error analysis for virtual targets are performed using the principle of stereo disparity. This universal simulation method provides a foundation for spatial design and image-plane adjustments for compound-eye cameras with specialized structures. Using the raw images captured by the compound-eye camera, a scene-specific piecewise linear mapping method is applied. This method significantly reduces the brightness contrast differences between sub-images during wide-field observations, enhancing image details. For the fast detection of moving targets, ommatidia clusters are defined as the minimal spatial constraint units. Local information at the centers of these constraint units is prioritized for processing. This approach replaces traditional global detection methods, improving the efficiency of subsequent processing. Finally, the simulated distance measurement results are validated using real-world scene data. Full article
►▼ Show Figures

Figure 1

25 pages, 12059 KiB  
Article
FasterGDSF-DETR: A Faster End-to-End Real-Time Fire Detection Model via the Gather-and-Distribute Mechanism
by Chengming Liu, Fan Wu and Lei Shi
Electronics 2025, 14(7), 1472; https://doi.org/10.3390/electronics14071472 - 6 Apr 2025
Viewed by 274
Abstract
Fire detection using deep learning has become a widely adopted approach. However, YOLO-based models often face performance limitations due to NMS, while DETR-based models struggle to meet real-time processing requirements. To address these challenges, we propose FasterGDSF-DETR, a novel fire detection model built [...] Read more.
Fire detection using deep learning has become a widely adopted approach. However, YOLO-based models often face performance limitations due to NMS, while DETR-based models struggle to meet real-time processing requirements. To address these challenges, we propose FasterGDSF-DETR, a novel fire detection model built upon the RT-DETR framework, designed to enhance both detection accuracy and efficiency. Firstly, this model introduces the FasterDBBNet backbone, which efficiently captures and retains feature information, accelerating the model’s convergence speed. Secondly, we propose the AIFI-GDSF hybrid encoder to reduce information loss in intra-scale interactions and improve the capability of detecting varying morphological flames. Furthermore, to better adapt to complex fire scenarios, we expand the dataset based on the KMU Fire and Smoke database and incorporate WIoU as the loss function to improve model robustness. Experimental results demonstrate that our proposed model surpasses mainstream object detection models in both accuracy and computational efficiency. FasterGDSF-DETR achieves a mean Average Precision of 71.5% on the self-constructed dataset, outperforming the YOLOv9 model of the same scale by 2.4 percentage points. This study introduces a novel task-specific enhancement to the RT-DETR framework, offering valuable insights for future advancements in fire detection technology. Full article
(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)
►▼ Show Figures

Figure 1

21 pages, 9797 KiB  
Article
Artificial Intelligence-Driven Optimal Charging Strategy for Electric Vehicles and Impacts on Electric Power Grid
by Umar Jamil, Raul Jose Alva, Sara Ahmed and Yu-Fang Jin
Electronics 2025, 14(7), 1471; https://doi.org/10.3390/electronics14071471 - 6 Apr 2025
Viewed by 468
Abstract
Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an [...] Read more.
Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs. Full article
(This article belongs to the Special Issue Recent Advances in Modeling and Control of Electric Energy Systems)
►▼ Show Figures

Figure 1

19 pages, 4715 KiB  
Article
Fuzzy Battery Manager: Charging and Balancing Rechargeable Battery Cells with Fuzzy Logic
by Adnan K. Shaout and Zachary Brauchler
Electronics 2025, 14(7), 1470; https://doi.org/10.3390/electronics14071470 - 6 Apr 2025
Viewed by 267
Abstract
This paper presents the design, implementation, and testing of a fuzzy battery manager featuring a novel hardware design. The system uses a fuzzy inference system to charge and balance two battery cells in series, integrating a microcontroller and a battery charging IC to [...] Read more.
This paper presents the design, implementation, and testing of a fuzzy battery manager featuring a novel hardware design. The system uses a fuzzy inference system to charge and balance two battery cells in series, integrating a microcontroller and a battery charging IC to demonstrate battery management with real hardware. It supports two battery chemistries, showcasing how the fuzzy system can be flexibly adapted to different rechargeable battery technologies. The fuzzy battery manager successfully achieves its goal of charging and balancing cells with high adaptability by simply adjusting membership functions. Its stability and effectiveness on real hardware have been confirmed. This adaptability offers significant potential across various industries. For example, a replacement battery pack designed for longevity using LiFePO4 cells could serve as an alternative to Li-Ion cells in electric vehicles, especially since LiFePO4 cells endure many more charge cycles, albeit with lower charge densities. The required membership functions for this replacement battery could be stored in just a few bytes of ROM within the battery pack, enabling seamless integration and use with existing vehicles and charging systems. Full article
(This article belongs to the Special Issue Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling)
►▼ Show Figures

Figure 1

21 pages, 8405 KiB  
Article
YOLOv11-BSS: Damaged Region Recognition Based on Spatial and Channel Synergistic Attention and Bi-Deformable Convolution in Sanding Scenarios
by Yinjiang Li, Zhifeng Zhou and Ying Pan
Electronics 2025, 14(7), 1469; https://doi.org/10.3390/electronics14071469 - 5 Apr 2025
Viewed by 251
Abstract
In order to address the problem that the paint surface of the damaged region of the body is similar to the color texture characteristics of the usual paint surface, which leads to the phenomenon of leakage or misdetection in the detection process, an [...] Read more.
In order to address the problem that the paint surface of the damaged region of the body is similar to the color texture characteristics of the usual paint surface, which leads to the phenomenon of leakage or misdetection in the detection process, an algorithm for detecting the damaged region of the body based on the improved YOLOv11 is proposed. Firstly, bi-deformable convolution is proposed to optimize the convolution kernel shape offset direction, which effectively improves the feature representation power of the backbone network; secondly, the C2PSA-SCSA module is designed to construct the coupling between spatial attention and channel attention, which enhances the perceptual power of the backbone network, and makes the model pay better attention to the damaged region features. Then, based on the GSConv module and the DWConv module, we build the slim-neck feature fusion network based on the GSConv module and DWConv module, which effectively fuses local features and global features to improve the saturation of semantic features; finally, the Focaler-CIoU border loss function is designed, which makes use of the principle of Focaler-IoU segmented linear mapping, adjusts the border loss function’s attention to different samples, and improves the model’s convergence of feature learning at various scales. The experimental results show that the enhanced YOLOv11-BSS network improves the precision rate by 7.9%, the recall rate by 1.4%, and the mAP@50 by 3.7% over the baseline network, which effectively reduces the leakage and misdetection of the damaged areas of the car body. Full article
►▼ Show Figures

Figure 1

17 pages, 6487 KiB  
Article
A Cost-Effective System for EMG/MMG Signal Acquisition
by Jerzy S. Witkowski and Andrzej Grobelny
Electronics 2025, 14(7), 1468; https://doi.org/10.3390/electronics14071468 - 5 Apr 2025
Viewed by 320
Abstract
This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the [...] Read more.
This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the primary goal of this project was to develop a simple and affordable system for simultaneous EMG and MMG data acquisition, offering efficiency comparable to commercial systems. The system consists of eight EMG/MMG probes, 16-bit analog-to-digital converters with 16 channels, and a microprocessor unit. Despite its multiple components, the system remains simple and user-friendly. This paper describes the construction of the EMG/MMG probe and analyzes the intrinsic noise of the preamplifier, as well as electromagnetic interference, particularly power line noise. The elimination of power line noise was carried out in two stages: first, using techniques known for electromagnetic compatibility (EMC), and second, by implementing a digital filter in the microprocessor system. The proposed solution enables direct data collection from eight EMG/MMG probes using any computer equipped with a USB interface. This interface facilitates both data transmission and power supply, making EMG/MMG data acquisition straightforward and efficient. Full article
(This article belongs to the Section Bioelectronics)
►▼ Show Figures

Figure 1

26 pages, 6966 KiB  
Article
Applying Collaborative Co-Simulation to Railway Traction Energy Consumption
by David Golightly, Anirban Bhattacharyya, Ken Pierce, Zhongbei Tian, Zhiyuan Lin, Ronghui Liu, Xinnan Lyu, Kangrui Jiang and Xiao Liu
Electronics 2025, 14(7), 1467; https://doi.org/10.3390/electronics14071467 - 5 Apr 2025
Viewed by 219
Abstract
Simulation is a vital tool for understanding rail traction energy consumption. Simulating such energy consumption requires an understanding of the interactions between timetable, infrastructure, and driver behavior to be encapsulated within a multi-train system model. This is critical to simulating systemic interactions that [...] Read more.
Simulation is a vital tool for understanding rail traction energy consumption. Simulating such energy consumption requires an understanding of the interactions between timetable, infrastructure, and driver behavior to be encapsulated within a multi-train system model. This is critical to simulating systemic interactions that affect energy consumption on a rail network. However, building and executing such a system simulation is challenging because of diverse models, stakeholders, and knowledge, as well as a lack of tools to support flexible and scalable simulation. This paper presents a demonstration of co-simulation—an approach originating in the automotive industry and now being used in other sectors—that enables a system model to be assessed for different configurations of timetable, rolling stock, infrastructure, and driver behavior. This paper describes the co-simulation approach before outlining the development process that allowed three research institutes, each with diverse models, to collaborate and deliver an integrated, holistic modeling approach. The results of this work are presented and discussed, both in terms of the quantified outputs and findings for energy consumption, and the lessons learned through collaborative co-simulation. Future avenues to build on this work are identified. Full article
(This article belongs to the Special Issue Railway Traction Power Supply, 2nd Edition)
►▼ Show Figures

Figure 1

25 pages, 11061 KiB  
Article
Integrated Sliding Mode Control for Permanent Magnet Synchronous Motor Drives Based on Second-Order Disturbance Observer and Low-Pass Filter
by Tran Thanh Tuyen, Jian Yang, Liqing Liao and Jingyang Zhou
Electronics 2025, 14(7), 1466; https://doi.org/10.3390/electronics14071466 - 5 Apr 2025
Viewed by 298
Abstract
This article presents an improved control strategy based on the traditional sliding-mode controller (SMC), integrated with a generalized higher-order disturbance observer (DOB), to enhance the speed regulation of permanent magnet synchronous motors (PMSMs) during operation. The proposed method is mitigated and employed to [...] Read more.
This article presents an improved control strategy based on the traditional sliding-mode controller (SMC), integrated with a generalized higher-order disturbance observer (DOB), to enhance the speed regulation of permanent magnet synchronous motors (PMSMs) during operation. The proposed method is mitigated and employed to smooth system disturbances by utilizing the disturbance observer (DOB) in conjunction with a low-pass filter (LPF). The low-pass filter is employed to smooth the q-axis current component and reduce speed oscillations. Initially, the paper builds upon the conventional control law and introduces a more optimized approach. The stability of the control strategy is then analyzed using Lyapunov stability theory. Different sliding surfaces are compared to develop the proposed SMC. Finally, the novel control method is introduced by integrating the DOB with the LPF. This approach results in improved speed stability and enhanced adaptability compared to traditional SMC techniques. Simulation and experimental results demonstrate that the proposed control algorithm outperforms traditional methods, particularly in terms of the dynamic response and disturbance rejection. Full article
(This article belongs to the Section Systems & Control Engineering)
►▼ Show Figures

Figure 1

23 pages, 665 KiB  
Article
TPH-Fuzz: A Two-Phase Hybrid Fuzzing Framework for Smart Contract Vulnerability Detection
by Fanglei Shi, Jinsheng Yang and Zhaohui Guo
Electronics 2025, 14(7), 1465; https://doi.org/10.3390/electronics14071465 - 5 Apr 2025
Viewed by 275
Abstract
Blockchain technology is revolutionizing various industries through decentralized architecture and secure transaction mechanisms, yet its core application—smart contracts—faces increasingly sophisticated security threats. Recognizing the critical need for enhanced protection in this emerging domain, this paper introduces TPH-Fuzz, a two-phase hybrid fuzzing framework designed [...] Read more.
Blockchain technology is revolutionizing various industries through decentralized architecture and secure transaction mechanisms, yet its core application—smart contracts—faces increasingly sophisticated security threats. Recognizing the critical need for enhanced protection in this emerging domain, this paper introduces TPH-Fuzz, a two-phase hybrid fuzzing framework designed to overcome current limitations in vulnerability detection. TPH-Fuzz combines global exploration with local vulnerability targeting. It utilizes dynamic symbolic execution for semantics-aware path analysis and employs data-dependency-based state modeling to generate effective transaction sequences. These methods improve both path exploration and vulnerability detection precision significantly. Experiments on a coverage dataset of 9309 contracts demonstrate an 85% branch coverage on complex contracts, outperforming conventional methods; meanwhile, tests on a vulnerability dataset of 1086 labeled contracts show a detection precision of 89.24% across eight vulnerability categories. The promising results underscore the framework’s potential to transform security auditing practices in the blockchain industry, paving the way for more reliable smart contract development and deployment. Full article
(This article belongs to the Section Computer Science & Engineering)
►▼ Show Figures

Figure 1

22 pages, 3427 KiB  
Article
A Multimodal Artificial Intelligence Model for Depression Severity Detection Based on Audio and Video Signals
by Liyuan Zhang, Shuai Zhang, Xv Zhang and Yafeng Zhao
Electronics 2025, 14(7), 1464; https://doi.org/10.3390/electronics14071464 - 4 Apr 2025
Viewed by 369
Abstract
In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models [...] Read more.
In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models are often too large for lightweight deployment, restricting their real-time monitoring capabilities, especially in resource-constrained environments. To address these challenges, this study proposes a lightweight and accurate multimodal method for detecting depression severity, aiming to provide effective support for smart healthcare systems. Specifically, we design a multimodal detection network based on speech and video signals, enhancing the recognition of depression severity by optimizing the cross-modal fusion strategy. The model leverages Long Short-Term Memory (LSTM) networks to capture long-term dependencies in speech and visual sequences, effectively extracting dynamic features associated with depression. Considering the behavioral differences of respondents when interacting with human versus robotic interviewers, we train two separate sub-models and fuse their outputs using a Mixture of Experts (MOE) framework capable of modeling uncertainty, thereby suppressing the influence of low-confidence experts. In terms of the loss function, the traditional Mean Squared Error (MSE) is replaced with Negative Log-Likelihood (NLL) to better model prediction uncertainty and enhance robustness. The experimental results show that the improved AI model achieves an accuracy of 83.86% in depression severity recognition. The model’s floating-point operations per second (FLOPs) reached 0.468 GFLOPs, with a parameter size of only 0.52 MB, demonstrating its compact size and strong performance. These findings underscore the importance of emotion and facial recognition in AI applications for mental health, offering a promising solution for real-time depression monitoring in resource-limited environments. Full article
(This article belongs to the Special Issue AI for Science: Advanced Techniques and Interdisciplinary Applications)
►▼ Show Figures

Figure 1

25 pages, 833 KiB  
Article
Harnessing Large Language Models for Automated Software Testing: A Leap Towards Scalable Test Case Generation
by Shaheer Rehan, Baidaa Al-Bander and Amro Al-Said Ahmad
Electronics 2025, 14(7), 1463; https://doi.org/10.3390/electronics14071463 - 4 Apr 2025
Viewed by 821
Abstract
Software testing is critical for ensuring software reliability, with test case generation often being resource-intensive and time-consuming. This study leverages the Llama-2 large language model (LLM) to automate unit test generation for Java focal methods, demonstrating the potential of AI-driven approaches to optimize [...] Read more.
Software testing is critical for ensuring software reliability, with test case generation often being resource-intensive and time-consuming. This study leverages the Llama-2 large language model (LLM) to automate unit test generation for Java focal methods, demonstrating the potential of AI-driven approaches to optimize software testing workflows. Our work leverages focal methods to prioritize critical components of the code to produce more context-sensitive and scalable test cases. The dataset, comprising 25,000 curated records, underwent tokenization and QLoRA quantization to facilitate training. The model was fine-tuned, achieving a training loss of 0.046. These results show the promise of AI-driven test case generation and underscore the feasibility of using fine-tuned LLMs for test case generation, highlighting opportunities for improvement through larger datasets, advanced hyperparameter optimization, and enhanced computational resources. We conducted a human-in-the-loop validation on a subset of unit tests generated by our fined-tuned LLM. This confirms that these tests effectively leverage focal methods, demonstrating the model’s capability to generate more contextually accurate unit tests. The work suggests the need to develop novel validation objective metrics specifically tailored for the automation of test cases generated by utilizing large language models. This work establishes a foundation for scalable and efficient software testing solutions driven by artificial intelligence. The data and code are publicly available on GitHub. Full article
(This article belongs to the Special Issue Intelligent Approaches for Solving Software Problems with AI Techniques)
►▼ Show Figures

Figure 1

24 pages, 11440 KiB  
Article
Research on Estimation Optimization of State of Charge of Lithium-Ion Batteries Based on Kalman Filter Algorithm
by Tian Xia, Xiangyang Xia, Jiahui Yue, Yu Gong, Jianguo Tan and Lixing Wen
Electronics 2025, 14(7), 1462; https://doi.org/10.3390/electronics14071462 - 4 Apr 2025
Viewed by 247
Abstract
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order [...] Read more.
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO’s fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Energy Conversion Systems)
►▼ Show Figures

Figure 1

14 pages, 16149 KiB  
Article
Modeling and Optimization of Structural Tuning in Bandgap-Engineered Tunneling Oxide for 3D NAND Flash Application
by Zhihong Xu, Shibo Xie, Zhijun Ying, Wenlong Zhang and Liming Gao
Electronics 2025, 14(7), 1461; https://doi.org/10.3390/electronics14071461 - 4 Apr 2025
Viewed by 326
Abstract
The bandgap-engineered tunneling oxide (BE-TOX) structure has been proposed to address the incompatibility between erase efficiency and retention performance in NAND flash memory. Previous studies have primarily focused on single flash memory cells, whose architecture significantly differs from that of 3D NAND flash [...] Read more.
The bandgap-engineered tunneling oxide (BE-TOX) structure has been proposed to address the incompatibility between erase efficiency and retention performance in NAND flash memory. Previous studies have primarily focused on single flash memory cells, whose architecture significantly differs from that of 3D NAND flash memory. Thus, the BE-TOX structure requires further research and optimization to improve device performance. In this study, the impact of varying proportions of the SiO2/SiOxNy/SiO2 (O1/N/O2) structure on performance is investigated using Technology Computer-Aided Design (TCAD) simulations. The results indicate that as the thickness of the N layer increases, the program/erase (P/E) speed improves, but reliability deteriorates. By adjusting the ratio of the O1 and O2 layers, the P/E speed can be optimized, and an optimal thickness can be identified. The simulation results demonstrate that the phenomenon is attributed to the combined effects of different barrier heights for charge tunneling and variations in band bending across the material layers. This study paves the way for further designing BE-TOX structures with balanced P/E performance and reliability. Full article
(This article belongs to the Section Electronic Materials, Devices and Applications)
►▼ Show Figures

Figure 1

18 pages, 5124 KiB  
Article
Influence of Electro-Optical Characteristics on Color Boundaries
by Jingxu Li, Xifeng Zheng, Deju Huang, Fengxia Liu, Junchang Chen, Yufeng Chen, Hui Cao and Yu Chen
Electronics 2025, 14(7), 1460; https://doi.org/10.3390/electronics14071460 - 4 Apr 2025
Viewed by 207
Abstract
This paper presents a comprehensive investigation into the phenomenon of gamut boundary distortion that occurs during the gamut conversion process in LED full-color display systems. This phenomenon is influenced by the electro-optical transfer function. First, a CIE-xyY colorimetric framework specifically designed for LEDs [...] Read more.
This paper presents a comprehensive investigation into the phenomenon of gamut boundary distortion that occurs during the gamut conversion process in LED full-color display systems. This phenomenon is influenced by the electro-optical transfer function. First, a CIE-xyY colorimetric framework specifically designed for LEDs is developed and established as the foundation for gamut conversion in LED applications. Next, the principles of gamut conversion based on this model are detailed. Additionally, a set of indices, including the Laplacian operator, entropy function, and magnitude of deviation of distorted color points, is integrated to form a comprehensive descriptive methodology. This methodology enables a thorough quantification of distribution patterns and effectively illustrates the outcomes of distortion. The findings of this research are significant for improving color conversion strategies and enhancing the color performance of display devices, making meaningful contributions to related fields. Full article
►▼ Show Figures

Figure 1

24 pages, 2024 KiB  
Article
An IoT Featureless Vulnerability Detection and Mitigation Platform
by Sarah Bin Hulayyil and Shancang Li
Electronics 2025, 14(7), 1459; https://doi.org/10.3390/electronics14071459 - 4 Apr 2025
Viewed by 415
Abstract
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) [...] Read more.
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) algorithms, which are deep learning and large language models, to uncover IoT security vulnerabilities based on network traffic data directly without manual feature selection. The platform correctly identifies vulnerable and secure IoT devices just by raw network traffic! Experimental results show that the proposed study detects vulnerability with great accuracy by using pre-trained deep learning and LLM models, which facilitates direct extraction of vulnerability features from the dataset and therefore helps speed up the identification process. In addition, the design of the platform ensures that the models are accessible and can be easily applied by users with a user-friendly interface. Furthermore, the models with small sizes, 277.5 MB and 334 MB for the deep learning model and the LLM model, respectively, illustrated the potential use of the detection tool in practical settings. The ability to defend large-scale, diversified IoT ecosystems efficiently and in a scalable way by installing thousands of software manifestations quickly while exposing new applications to growing cyber threats is made possible by this significant advancement in the field of IoT security. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
►▼ Show Figures

Figure 1

13 pages, 1868 KiB  
Review
Designs and Challenges in Fluid Antenna System Hardware
by Kin-Fai Tong, Baiyang Liu and Kai-Kit Wong
Electronics 2025, 14(7), 1458; https://doi.org/10.3390/electronics14071458 - 3 Apr 2025
Viewed by 302
Abstract
Fluid Antenna Systems (FASs) have recently emerged as a promising solution to address the demanding performance indicators (KPIs) and scalability challenges of future 6G mobile communications. By enabling agile control over both radiating position and antenna shape, FAS can significantly improve diversity gain [...] Read more.
Fluid Antenna Systems (FASs) have recently emerged as a promising solution to address the demanding performance indicators (KPIs) and scalability challenges of future 6G mobile communications. By enabling agile control over both radiating position and antenna shape, FAS can significantly improve diversity gain and reduce outage probability through dynamic selection of the optimal radiation point, also known as port. Numerous theoretical studies have explored novel FAS concepts, often in conjunction with other wireless communication technologies such as multiple-input multiple-output (MIMO), Non-Orthogonal Multiple Access (NOMA), Reconfigurable Intelligent Surfaces (RIS), Integrated Sensing and Communication (ISAC), backscatter communication, and Semantic communication. To validate these theoretical concepts, several early-stage FAS hardware prototypes have been developed, including liquid–metal fluid antennas, mechanically movable antennas, pixel-reconfigurable antennas, and meta-fluid antennas. Initial measurements have demonstrated the potential advantages of FAS. This article provides a brief review of these early FAS hardware technologies. Furthermore, we share our vision for future hardware development and the corresponding challenges, aiming to fully release the potential of FAS and stimulate further research and development within the antenna research community. Full article
(This article belongs to the Special Issue Futuristic Antennas: Sustainable, Efficient, Reconfigurable, and Intelligent Design)
►▼ Show Figures

Figure 1

20 pages, 5975 KiB  
Article
Fast Tongue Detection Based on Lightweight Model and Deep Feature Propagation
by Keju Chen, Yun Zhang, Li Zhong and Yongguo Liu
Electronics 2025, 14(7), 1457; https://doi.org/10.3390/electronics14071457 - 3 Apr 2025
Viewed by 230
Abstract
While existing tongue detection methods have achieved good accuracy, the problems of low detection speed and excessive noise in the background area still exist. To address these problems, a fast tongue detection model based on a lightweight model and deep feature propagation (TD-DFP) [...] Read more.
While existing tongue detection methods have achieved good accuracy, the problems of low detection speed and excessive noise in the background area still exist. To address these problems, a fast tongue detection model based on a lightweight model and deep feature propagation (TD-DFP) is proposed. Firstly, a color channel is added to the RGB tongue image to introduce more prominent tongue features. To reduce the computational complexity, keyframes are selected through inter frame differencing, while optical flow maps are used to achieve feature alignment between non-keyframes and keyframes. Secondly, a convolutional neural network with feature pyramid structures is designed to extract multi-scale features, and object detection heads based on depth-wise convolutions are adopted to achieve real-time tongue region detection. In addition, a knowledge distillation module is introduced to improve training performance during the training phase. TD-DFP achieved 82.8% mean average precision (mAP) values and 61.88 frames per second (FPS) values on the tongue dataset. The experimental results indicate that TD-DFP can achieve efficient and accurate tongue detection, achieving real-time tongue detection. Full article
(This article belongs to the Special Issue Mechanism and Modeling of Graph Convolutional Networks)
►▼ Show Figures

Figure 1

18 pages, 514 KiB  
Article
Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
by Guoyong Wang, Mengfei Fan, Saiwei Jia, Meiyi Yang, Xinxin Wei and Lin Wang
Electronics 2025, 14(7), 1456; https://doi.org/10.3390/electronics14071456 - 3 Apr 2025
Viewed by 168
Abstract
Flying ad hoc networks (FANETs) have highly dynamic and energy-limited characteristics. Compared with traditional mobile ad hoc networks, their nodes move faster and their topology changes more frequently. Therefore, the design of routing protocols faces greater challenges. The existing routing schemes rely on [...] Read more.
Flying ad hoc networks (FANETs) have highly dynamic and energy-limited characteristics. Compared with traditional mobile ad hoc networks, their nodes move faster and their topology changes more frequently. Therefore, the design of routing protocols faces greater challenges. The existing routing schemes rely on frequent and fixed-interval Hello transmissions, which exacerbates network load and leads to high communication energy consumption and outdated location information. MP-QGRD combined with the extended Kalman filter (EKF) is used for node position prediction, and the Hello packet transmission interval is dynamically adjusted to optimize neighbor discovery. At the same time, reinforcement learning methods are used to comprehensively consider link stability, energy consumption, and communication distance for routing decisions. The simulation results show that compared to QMR, QGeo, and GPSR, MP-QGRD has an increased packet delivery rate, end-to-end latency, and communication energy consumption by 10%, 30%, and 15%, respectively. Full article
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-249
Previous Issue
Next Issue
Back to TopTop