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Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data
Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems for Road Networks
Low-Power Silicon-Based Frequency Dividers: An Overview
Forecasting Corporate Financial Performance Using Deep Learning with Environmental, Social, and Governance Data
Wearable Displacement Sensor Using Inductive Coupling of Printed RFID Tag with Metallic Strip

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Physics, Applied) / CiteScore - Q2 (Control and Systems Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software and Microelectronics.

Impact Factor: 2.6 (2023); 5-Year Impact Factor: 2.6 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

14 pages, 7852 KiB

Open AccessArticle

Life Prediction Model for Press-Pack IGBT Module Based on Thermal Resistance Degradation

by Rui Zhou, Xiang Wang, Jianqiang Li, Tong An, Zhengqiang Yu, Xiaochen Wang and Yan Li

Electronics 2025, 14(9), 1726; https://doi.org/10.3390/electronics14091726 (registering DOI) - 24 Apr 2025

The contact interfaces of a press-pack insulated-gate bipolar transistor (PP-IGBT) module under fluctuating thermal stress will undergo minor friction and mutual sliding during service, which results in damage to the contact surface and a decline in the thermal performance of the contact interface. [...] Read more.

The contact interfaces of a press-pack insulated-gate bipolar transistor (PP-IGBT) module under fluctuating thermal stress will undergo minor friction and mutual sliding during service, which results in damage to the contact surface and a decline in the thermal performance of the contact interface. Therefore, the temperature inside the module will continue to increase, leading to eventual failure. In this work, a life prediction method based on thermal resistance degradation within a PP-IGBT module is established. The junction temperature can be determined via power loss and a resistance-capacitance (RC) thermal network model, and a life prediction model of the PP-IGBT module is developed based on thermal resistance degradation. The method considers the service quality under power cycling conditions and the influence of the self-accelerating effect of damage accumulation at the contact interface of the PP-IGBT module on fatigue life. The experimental results verify that the proposed PP-IGBT module life prediction method can effectively predict service life under power cycling conditions. Full article

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14 pages, 5999 KiB

Open AccessArticle

Frequency-Selective Surface Based 360-Degree Beam-Steerable Cavity Antenna for UAV Swarm Coordination

by Mashrur Zawad, Chandana Kolluru, Sohel Rana, Kalyan C. Durbhakula and Mohamed Z. M. Hamdalla

Electronics 2025, 14(9), 1725; https://doi.org/10.3390/electronics14091725 (registering DOI) - 24 Apr 2025

A swarm of unmanned aerial vehicles (UAVs) often rely on exceptional wireless coverage of embedded or flush-mounted antennas or arrays, especially in long-range communication. While arrays offer significant range and beam steerability control, they often suffer from size, weight, and power (SWaP) limitations. [...] Read more.

A swarm of unmanned aerial vehicles (UAVs) often rely on exceptional wireless coverage of embedded or flush-mounted antennas or arrays, especially in long-range communication. While arrays offer significant range and beam steerability control, they often suffer from size, weight, and power (SWaP) limitations. On the other hand, achieving a wideband, high-gain, and beam-steerable response from a single antenna is highly desired for its compact SWaP characteristics. In this study, a cube-shaped cavity antenna excited by a monopole feed is designed, fabricated, and measured. The proposed antenna operates from 4.1 to 5.56 GHz with a 30.22% fractional bandwidth and a peak gain of 8 dBi. In addition, a frequency-selective surface (FSS) is developed to replace the metallic faces of the cavity, enabling 360° electronic beam steerability. Thermal analysis of the FSS-based cavity design is conducted to determine its maximum power handling capability, revealing a maximum power handling capability of 1.3 KW continuous. In addition, the maximum rating currents of the FSS diodes can be reached only at 165 W, limiting the maximum power handling to only 165 W in the case of using the diodes used in this analysis. The antenna prototype is successfully fabricated, and the radiation pattern is experimentally measured, showing a strong agreement between the simulated and measured results. The electronic steerability of the proposed antenna indicates its suitability for 5G new radio and UAV applications. Full article

(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)

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16 pages, 8051 KiB

Open AccessArticle

Design and Joint Dynamics of Human Recumbent Rehabilitation Training Devices

by Qiulong Wu, Chaoyue Sun, Yi Liu, Sikai Wang, Jian Li and Peng Su

Electronics 2025, 14(9), 1724; https://doi.org/10.3390/electronics14091724 - 23 Apr 2025

(1) Background: Patients bedridden due to accidental injuries, diseases, or age-related functional impairments require accelerated recovery of autonomous limb movement. A prone-position rehabilitation training device was developed to provide training intensity tailored to patients’ motor capabilities. (2) Methods: Based on principles of human [...] Read more.

(1) Background: Patients bedridden due to accidental injuries, diseases, or age-related functional impairments require accelerated recovery of autonomous limb movement. A prone-position rehabilitation training device was developed to provide training intensity tailored to patients’ motor capabilities. (2) Methods: Based on principles of human prone limb motion mechanics and torque balance, this study analyzed joint torque during limb movements using optical motion capture and six-dimensional force plate data. Joint torque curves during prone-position training were simulated, and a prototype device was developed. Prototype assembly and experimental validation of device–human synergy was conducted. (3) Results: Comparative analysis of joint torques between healthy individuals and patients revealed that joint torque increases as limbs contract inward. The maximum torque for upper limb joints was approximately 3.5 Nm, while the knee joint torque reached around 40 Nm. (4) Conclusions: Prototype testing confirmed the device’s design rationality, meeting human–machine synergy and rehabilitation training intensity requirements. This study provides a reference for the design of prone-position rehabilitation training devices. Full article

(This article belongs to the Special Issue Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives)

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18 pages, 447 KiB

Open AccessArticle

A k-Means Algorithm with Automatic Outlier Detection

by Guojun Gan

Electronics 2025, 14(9), 1723; https://doi.org/10.3390/electronics14091723 - 23 Apr 2025

Data clustering is a fundamental machine learning task found in many real-world applications. However, real data usually contain noise or outliers. Handling outliers in a clustering algorithm can improve the clustering accuracy. In this paper, we propose a variant of the k-means [...] Read more.

Data clustering is a fundamental machine learning task found in many real-world applications. However, real data usually contain noise or outliers. Handling outliers in a clustering algorithm can improve the clustering accuracy. In this paper, we propose a variant of the k-means algorithm to provide data clustering and outlier detection simultaneously. In the proposed algorithm, outlier detection is integrated with the clustering process and is achieved via a term added to the objective function of the k-means algorithm. The proposed algorithm generates two partition matrices: one provides cluster groups and the other can be used to detect outliers. We use both synthetic data and real data to demonstrate the effectiveness and efficiency of the proposed algorithm and show that the clustering performance of the proposed approach is better than other, similar methods. Full article

(This article belongs to the Special Issue Knowledge Engineering and Data Mining, 3rd Edition)

28 pages, 7580 KiB

Open AccessArticle

Research on Consensus Algorithm for Intellectual Property Authentication Based on PBFT

by Jing Wang, Wenlong Feng, Mengxing Huang, Siling Feng and Dan Du

Electronics 2025, 14(9), 1722; https://doi.org/10.3390/electronics14091722 - 23 Apr 2025

Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristics of blockchain, such as decentralization, tampering, and traceability, these challenges can be effectively dealt with. [...] Read more.

Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristics of blockchain, such as decentralization, tampering, and traceability, these challenges can be effectively dealt with. Aiming at the shortcomings of traditional consensus algorithms in intellectual property authentication, such as high communication overhead and low efficiency, the improved PBFT (Practical Byzantine Fault Tolerance) algorithm (MBFT algorithm) is proposed and combined with the distributed database IPFS (Inter Planetary File System) to alleviate the pressure of blockchain data storage and enhance operational efficiency. The algorithm first adopts the evaluation system in the hierarchical mechanism, invokes the Fibonacci series incremental law to update the Score value of the nodes and sort them, and divides the nodes into the classification consensus layer, the consensus confirmation layer, and the supervision layer. Secondly, the Maglev algorithm is used to generate a lookup table and design a classification consensus strategy, which is divided into four consensus groups based on the characteristics of intellectual property categories, namely, the patent authentication consensus group, the trademark authentication consensus group, the copyright authentication consensus group, and the other types of authentication consensus group. Then, the algorithm optimizes the consistency protocol, carries out PBFT consensus once in each of the classification consensus layers and consensus confirmation layers, according to the consensus situation, and realizes the nodes’ dynamic update to ensure the consensus’s accuracy and reliability. The experiments show that the MBFT algorithm performs better in terms of communication complexity and throughput. As the number and size of files increase, the execution time of IPFS progressively lengthens. However, the overall performance still meets the actual demand. Compared with the traditional PBFT, MBFT improves the communication complexity by about 50% or more, the throughput is about 3 times the traditional PBFT, and the scalability and response speed of the system are significantly improved when the number of nodes increases. Full article

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48 pages, 1108 KiB

Open AccessReview

Artificial Intelligence vs. Efficient Markets: A Critical Reassessment of Predictive Models in the Big Data Era

by Antonio Pagliaro

Electronics 2025, 14(9), 1721; https://doi.org/10.3390/electronics14091721 - 23 Apr 2025

This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance [...] Read more.

This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance across different market regimes or reconcile statistical significance with economic relevance. We analyze techniques ranging from traditional statistical models to advanced deep learning architectures, finding that ensemble methods like Extra Trees, Random Forest, and XGBoost consistently outperform single classifiers, achieving directional accuracy of up to 86% in specific market conditions. Our analysis reveals that hybrid approaches integrating multiple data sources demonstrate superior performance by capturing complementary market signals, yet many models showing statistical significance fail to generate economic value after accounting for transaction costs and market impact. By addressing methodological challenges including backtest overfitting, regime changes, and implementation constraints, we provide a novel comprehensive framework for rigorous model assessment that bridges the divide between academic research and practical implementation. This review makes three key contributions: (1) a reconciliation of the Efficient Market Hypothesis with AI-driven predictability through an adaptive market framework, (2) a multi-dimensional evaluation methodology that extends beyond classification accuracy to financial performance, and (3) an identification of promising research directions in explainable AI, transfer learning, causal modeling, and privacy-preserving techniques that address current limitations. Full article

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

19 pages, 5743 KiB

Open AccessArticle

An Optimized Intelligent Segmentation Algorithm for Concrete Cracks Based on Transformer

by Tianhao Ye, Min He, Yexuan Wang, Jiaying Wang, Lei Zhu and Jie Zhang

Electronics 2025, 14(9), 1720; https://doi.org/10.3390/electronics14091720 - 23 Apr 2025

The accurate detection and segmentation of concrete cracks are crucial for maintaining the integrity and safety of infrastructure. Traditional manual inspection methods are often constrained by background complexity and environmental noise, while deep learning models face challenges related to data dependency and poor [...] Read more.

The accurate detection and segmentation of concrete cracks are crucial for maintaining the integrity and safety of infrastructure. Traditional manual inspection methods are often constrained by background complexity and environmental noise, while deep learning models face challenges related to data dependency and poor generalization in complex scenarios. To address these issues, this paper proposes an enhanced transFissNet model that integrates a ResNet101 backbone with Transformer modules for self-attention and multi-scale feature extraction. The model improves the robustness of crack detection under varying lighting conditions and irregular crack morphologies. Experimental results on multiple benchmark datasets demonstrate that transFissNet achieves an accuracy of 96.8%, outperforming existing mainstream methods. The proposed approach provides a reliable and scalable solution for automated crack segmentation and contributes to the advancement of intelligent structural health monitoring. Full article

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22 pages, 1864 KiB

Open AccessArticle

Research on Distributed Smart Home Energy Management Strategies Based on Non-Intrusive Load Monitoring (NILM)

by Siqi Liu, Zhiyuan Xie and Zhengwei Hu

Electronics 2025, 14(9), 1719; https://doi.org/10.3390/electronics14091719 - 23 Apr 2025

Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems. Simultaneously, it enhances energy autonomy, lowers carbon emissions, and promotes sustainable low-carbon [...] Read more.

Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems. Simultaneously, it enhances energy autonomy, lowers carbon emissions, and promotes sustainable low-carbon lifestyles. By coordinating demand response programs with flexible load scheduling strategies, this approach effectively reduces peak loads and improves grid stability, thereby advancing smart grid development. This paper investigates the optimized scheduling problem in smart home energy management systems, focusing on achieving integrated optimization of multiple factors, including load balancing, cost control, carbon emission reduction, user comfort, and demand response. Considering the diverse load characteristics of residential energy systems, we propose a novel optimization framework incorporating dynamic pricing mechanisms and intelligent scheduling algorithms, which is rigorously validated through simulation experiments. Results demonstrate that the proposed scheduling strategy successfully balances economic efficiency, load management, and environmental sustainability while maintaining acceptable user comfort levels—providing a comprehensive solution for intelligent home energy management systems. Full article

(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)

22 pages, 33905 KiB

Open AccessArticle

Flight Path Optimization for UAV-Aided Reconnaissance Data Collection

by Chen Xie, Chuan Gu, Binbin Wu and Daoxing Guo

Electronics 2025, 14(9), 1718; https://doi.org/10.3390/electronics14091718 - 23 Apr 2025

While UAV-aided reconnaissance data collection grows rapidly, its path optimization is severely hindered by environmental dynamics, node mobility, and overlapping security/interference risks. This paper addresses the problem of data collection from mobile reconnaissance nodes in interference environments, incorporating safety flight constraints and dynamic [...] Read more.

While UAV-aided reconnaissance data collection grows rapidly, its path optimization is severely hindered by environmental dynamics, node mobility, and overlapping security/interference risks. This paper addresses the problem of data collection from mobile reconnaissance nodes in interference environments, incorporating safety flight constraints and dynamic constraints. We propose an integrated path optimization framework combining initial path guidance with dynamic replanning capabilities. The system first predicts reconnaissance node and interference source positions while analyzing channel conditions to generate an optimal initial path within the designated communication flight corridor. Subsequently, the framework addresses non-convex path planning challenges using slack variables and equivalent constraints, with the successive convex approximation algorithm providing efficient iterative solutions. Numerical results show that in dynamically changing scenarios, the proposed method achieves high reliability and planning efficiency for real-time, data-collection-oriented path optimization. Full article

(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)

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31 pages, 1442 KiB

Open AccessArticle

Receipt Recognition Technology Driven by Multimodal Alignment and Lightweight Sequence Modeling

by Jin-Ming Yu, Hui-Jun Ma and Jian-Lei Kong

Electronics 2025, 14(9), 1717; https://doi.org/10.3390/electronics14091717 - 23 Apr 2025

With the rapid advancement of global digital transformation, enterprises and financial institutions face increasing challenges in managing and processing receipt-like financial documents. Traditional manual document processing methods can no longer meet the demands of modern office operations and business expansion. To address these [...] Read more.

With the rapid advancement of global digital transformation, enterprises and financial institutions face increasing challenges in managing and processing receipt-like financial documents. Traditional manual document processing methods can no longer meet the demands of modern office operations and business expansion. To address these issues, automated document recognition systems based on computer vision and deep learning technologies have emerged. This paper proposes a receipt recognition technology based on multimodal alignment and lightweight sequence modeling, integrating the CLIP (Contrastive Language-Image Pretraining) and Bidirectional Gated Recurrent Unit (BiGRU) framework. The framework aims to achieve synergistic optimization of image and text information through semantic correction. By leveraging dynamic threshold classification, geometric regression loss, and multimodal feature alignment, the framework significantly improves text detection and recognition accuracy in complex layouts and low-quality images. Experimental results show that the model achieves a detection F1 score of 93.1% and a Character Error Rate (CER) of 5.1% on the CORD dataset. Through a three-stage compression strategy of quantization, pruning, and distillation, the model size is reduced to 18 MB, achieving real-time inference speeds of 25 FPS on the Jetson AGX Orin edge device, with power consumption stabilized below 12 W. This framework provides an efficient, accurate, and edge-computing-friendly solution for automated receipt processing. Practical implications include its potential to enhance the efficiency of financial audits, improve tax compliance, and streamline the operational management of financial institutions, making it a valuable tool for real-world applications in receipt automation. Full article

22 pages, 1959 KiB

Open AccessArticle

DMFormer: Dense Memory Linformer for Image Captioning

by Yuting He and Zetao Jiang

Electronics 2025, 14(9), 1716; https://doi.org/10.3390/electronics14091716 - 23 Apr 2025

Image captioning is a cross-task of computer vision and natural language processing, aiming to describe image content in natural language. Existing methods still have deficiencies in modeling the spatial location and semantic correlation between image regions. Furthermore, these methods often exhibit insufficient interaction [...] Read more.

Image captioning is a cross-task of computer vision and natural language processing, aiming to describe image content in natural language. Existing methods still have deficiencies in modeling the spatial location and semantic correlation between image regions. Furthermore, these methods often exhibit insufficient interaction between image features and text features. To address these issues, we propose a Linformer-based image captioning method, the Dense Memory Linformer for Image Captioning (DMFormer), which has lower time and space complexity than the traditional Transformer architecture. The DMFormer contains two core modules: the Relation Memory Augmented Encoder (RMAE) and the Dense Memory Augmented Decoder (DMAD). In the RMAE, we propose Relation Memory Augmented Attention (RMAA), which combines explicit spatial perception and implicit spatial perception. It explicitly uses geometric information to model the geometric correlation between image regions and implicitly constructs memory unit matrices to learn the contextual information of image region features. In the DMAD, we introduce Dense Memory Augmented Cross Attention (DMACA). This module fully utilizes the low-level and high-level features generated by the RMAE through dense connections, and constructs memory units to store prior knowledge of image and text. It learns the cross-modal associations between visual and linguistic features through an adaptive gating mechanism. Experimental results on the MS-COCO dataset show that the descriptions generated by the DMFormer are richer and more accurate, with significant improvements in various evaluation metrics compared to mainstream methods. Full article

(This article belongs to the Special Issue AI/Machine Learning in Computer Vision/Image Processing and Natural Language Processing)

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8 pages, 2260 KiB

Open AccessArticle

Wafer-Level Amplitude Equalizer Based on an Integrated Passive Device Process with Two Resonance Points for Wavy In-Band Transmission

by Xiaodong Yang, Mengjiang Xing, Gan Liu, Xiaozhen Li, Xiangyu Sun, Wenzhi Liu and Yaobing Lu

Electronics 2025, 14(9), 1715; https://doi.org/10.3390/electronics14091715 - 23 Apr 2025

Amplitude equalizers play an important role in microwave transmission systems, and their performance can directly improve the signal transmission quality. Nowadays, the miniaturized equalizer is in good need of a highly integrated radio frequency (RF) front end. In this paper, a wafer-level amplitude [...] Read more.

Amplitude equalizers play an important role in microwave transmission systems, and their performance can directly improve the signal transmission quality. Nowadays, the miniaturized equalizer is in good need of a highly integrated radio frequency (RF) front end. In this paper, a wafer-level amplitude equalizer based on an integrated passive device (IPD) process is proposed. Moreover, the equalizer circuit containing two resonance points within the transmission frequency band is also proposed. The amplitude equalizer operates at the center frequency of 3.88 GHz, with two resonance points of 2.9 GHz and 5.2 GHz; the minimum insertion loss is 1.17 dB, the maximum attenuation is 4 dB, and the in-band voltage standing wave ratio (VSWR) is less than 1.6:1. All the measured results are in good agreement with the designed results. The size of the proposed equalizer is 0.8 mm × 0.65 mm × 0.1 mm (0.011λ × 0.008λ × 0.001λ), which shows great potential in the miniaturization application of the RF microsystem. Furthermore, the new equalizer circuit with two resonance points is especially suitable for wavy in-band transmission. Full article

(This article belongs to the Section Microelectronics)

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22 pages, 1396 KiB

Open AccessArticle

Research on Data Ownership and Controllable Sharing Schemes in the Process of Logistics Data Flow

by Ziqi Liu, Zhanling Shi, Wenjing Wang, Rui Kong, Deqian Fu and Jianlong Qiu

Electronics 2025, 14(9), 1714; https://doi.org/10.3390/electronics14091714 - 23 Apr 2025

The secure and effective dissemination of logistics data is frequently obstructed by issues pertaining to ownership verification and unwanted access during data exchange. This study introduces an innovative strategy for data ownership verification and regulated sharing based on the notion of “three rights [...] Read more.

The secure and effective dissemination of logistics data is frequently obstructed by issues pertaining to ownership verification and unwanted access during data exchange. This study introduces an innovative strategy for data ownership verification and regulated sharing based on the notion of “three rights separation”, which delineates data ownership into control rights, usage rights, and management rights. The proposed approach incorporates chameleon signatures and blockchain technology to facilitate dynamic, non-falsifiable ownership marking and verification. Additionally, it utilizes searchable encryption and proxy re-encryption methods to guarantee that data are viewed solely under allowed circumstances, thereby averting misuse by third-party data administrators. Security analysis verifies resilience against adaptive chosen-message assaults and keyword-guessing threats, while simulation studies illustrate the scheme’s robustness, efficiency, and practical applicability in real-world logistical settings. This framework offers a scalable and safe solution for multi-party data sharing with explicitly defined ownership control. Full article

(This article belongs to the Special Issue Recent Advances in Cybersecurity and Information Security)

22 pages, 2567 KiB

Open AccessArticle

FA-YOLO: A Pedestrian Detection Algorithm with Feature Enhancement and Adaptive Sparse Self-Attention

by Hang Sui, Huiyan Han, Yuzhu Cui, Menglong Yang and Binwei Pei

Electronics 2025, 14(9), 1713; https://doi.org/10.3390/electronics14091713 - 23 Apr 2025

Pedestrian detection technology refers to identifying pedestrians within the field of view and is widely used in smart cities, public safety surveillance, and other scenarios. However, in real-world complex scenes, challenges such as high pedestrian density, occlusion, and low lighting conditions lead to [...] Read more.

Pedestrian detection technology refers to identifying pedestrians within the field of view and is widely used in smart cities, public safety surveillance, and other scenarios. However, in real-world complex scenes, challenges such as high pedestrian density, occlusion, and low lighting conditions lead to blurred image boundaries, which significantly impact accuracy of pedestrian detection. To address these challenges, we propose a novel pedestrian detection algorithm, FA-YOLO. First, to address issues of limited effective information extraction in backbone network and insufficient feature map representation, we propose a feature enhancement module (FEM) that integrates both global and local features of the feature map, thereby enhancing the network’s feature representation capability. Then, to reduce redundant information and improve adaptability to complex scenes, an adaptive sparse self-attention (ASSA) module is designed to suppress noise interactions in irrelevant regions and eliminate feature redundancy across both spatial and channel dimensions. Finally, to further enhance the model’s focus on target features, we propose cross stage partial with adaptive sparse self-attention (C3ASSA), which improves overall detection performance by reinforcing the importance of target features during the final detection stage. Additionally, a scalable intersection over union (SIoU) loss function is introduced to address the vector angle differences between predicted and ground-truth bounding boxes. Extensive experiments on the WiderPerson and RTTS datasets demonstrate that FA-YOLO achieves State-of-the-Art performance, with a precision improvement of 3.5% on the WiderPerson and 3.0% on RTTS compared to YOLOv11. Full article

(This article belongs to the Special Issue Applications of Computer Vision, 3rd Edition)

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24 pages, 11613 KiB

Open AccessArticle

An Experimental Review of Step-Down Converter Topologies with Wide Input Voltage Range for Modern Vehicle Low-Power Systems

by Lukas Šalavėjus, Vaidotas Barzdėnas, Aleksandr Vasjanov and Tzung-Je Lee

Electronics 2025, 14(9), 1712; https://doi.org/10.3390/electronics14091712 - 23 Apr 2025

This study explored wide input voltage range step-down converters, which are crucial for supplying low-voltage power to microcontrollers, communication modems, and other low-power electronic circuits in modern vehicles that feature a diverse range of battery voltages, from 12 V to 90 V. Specifically, [...] Read more.

This study explored wide input voltage range step-down converters, which are crucial for supplying low-voltage power to microcontrollers, communication modems, and other low-power electronic circuits in modern vehicles that feature a diverse range of battery voltages, from 12 V to 90 V. Specifically, it focuses on converters suitable for devices employing 2G to 5G communication modems that typically require a 4 V supply. This article conducted a review of existing step-down converter technologies, focusing on the comparative analysis of synchronous versus asynchronous switched-inductor converters. Experimental data on 14 different converters is presented, where theoretical efficiency predictions are compared with empirical measurements. Using the dataset of 326 points gathered through experimental evaluation enabled evaluation of the accuracy of the efficiency estimating mathematical model, which demonstrated 73% accuracy for input voltages ranging from 12 V to 90 V and output currents from 0.1 A to 2 A. Further analysis demonstrated that estimating power losses in milliwatts enhanced the model’s accuracy, as indicated by a 94% correlation between measured and calculated power losses and an 87% correlation of measured power losses with measured temperature. This study provides insights that will guide both the authors and readers in developing more efficient DC-DC step-down converters, optimized for modern automotive applications. Full article

(This article belongs to the Section Power Electronics)

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25 pages, 12348 KiB

Open AccessArticle

A Novel Modified Delta-Connected CHB Multilevel Inverter with Improved Line–Line Voltage Levels

by Abdullah M. Noman

Electronics 2025, 14(9), 1711; https://doi.org/10.3390/electronics14091711 - 23 Apr 2025

Numerous cascaded inverter configurations have been developed to generate higher voltage levels, thereby improving performance and lowering costs. Comparing conventional delta-connected cascaded H-bridge (CHB) multilevel inverters to star-connected CHB multilevel inverters reveals a disadvantage. In conventional delta-connected CHB multilevel inverters, more switches are [...] Read more.

Numerous cascaded inverter configurations have been developed to generate higher voltage levels, thereby improving performance and lowering costs. Comparing conventional delta-connected cascaded H-bridge (CHB) multilevel inverters to star-connected CHB multilevel inverters reveals a disadvantage. In conventional delta-connected CHB multilevel inverters, more switches are unavoidably needed to achieve the same line-to-line grid voltage, since more H-bridges cascaded in series are required than in a star-connected CHB. This paper presents a modified topology based on the delta-connected CHB multilevel configuration to provide the same number of line-to-line voltage levels as a star-connected CHB, using an equivalent number of switches. The number of switches in the proposed multilevel inverter is decreased compared to conventional delta-connected CHB MLIs at the same voltage levels. The mathematical modeling of the proposed topology and the simulation results using a fixed load and a PV-grid connection are provided to validate the efficacy and dependability of the proposed topology. To validate the usefulness of the proposed configuration, it was practically implemented in the laboratory. Data acquisition and generation of gating signals to fire the switches were implemented using a MicroLabBox real-time controller. The prototype was examined under a resistive–inductive load and tested under different modulation indices. To demonstrate the effectiveness and the functionality of the topology, the experimental results are also provided. Full article

(This article belongs to the Special Issue Power Electronics in Renewable Systems)

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24 pages, 7088 KiB

Open AccessArticle

Ultra-Lightweight and Highly Efficient Pruned Binarised Neural Networks for Intrusion Detection in In-Vehicle Networks

by Auangkun Rangsikunpum, Sam Amiri and Luciano Ost

Electronics 2025, 14(9), 1710; https://doi.org/10.3390/electronics14091710 - 23 Apr 2025

With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, [...] Read more.

With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, their heavy computational and power demands often limit real-world deployment. In this paper, we present an optimised IDS based on a Binarised Neural Network (BNN) that employs network pruning to eliminate redundant parameters, achieving up to a 91.07% reduction with only a 0.1% accuracy loss. The proposed approach incorporates a two-stage Coarse-to-Fine (C2F) framework, efficiently filtering normal traffic in the initial stage to minimise unnecessary processing. To assess its practical feasibility, we implement and compare the pruned IDS across CPU, GPU, and FPGA platforms. The experimental results indicate that, with the same model structure, the FPGA-based solution outperforms GPU and CPU implementations by up to 3.7× and 2.4× in speed, while achieving up to 7.4× and 3.8× greater energy efficiency, respectively. Among cutting-edge BNN-based IDSs, our ultra-lightweight FPGA-based C2F approach achieves the fastest average inference speed, showing a 3.3× to 12× improvement, while also outperforming them in accuracy and average F1 score, highlighting its potential for low-power, high-performance vehicle security. Full article

(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)

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18 pages, 1621 KiB

Open AccessArticle

Performance Optimization of Machine-Learning Algorithms for Fault Detection and Diagnosis in PV Systems

by Eduardo Quiles-Cucarella, Pedro Sánchez-Roca and Ignacio Agustí-Mercader

Electronics 2025, 14(9), 1709; https://doi.org/10.3390/electronics14091709 - 23 Apr 2025

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and [...] Read more.

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Point Tracking (MPPT) and Limited Power Point Tracking (LPPT) conditions to determine their adaptability. The results indicate that the ensemble bagged tree classifier achieves the highest accuracy (92.2%) across all fault scenarios, while neural network-based models perform better under MPPT conditions. Additionally, the study highlights variations in model performance based on power mode, suggesting the potential for adaptive diagnostic approaches. The findings reinforce the feasibility of machine learning for predictive maintenance in PV systems, offering a cost-effective, sensor-free method for real-time fault detection. Future research should explore hybrid models that dynamically switch between classifiers based on system conditions, as well as validation using real-world PV installations. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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13 pages, 8115 KiB

Open AccessArticle

Laser-Powered Harvesting Tool for Tabletop Grown Strawberries

by Mohamed Sorour and Pål From

Electronics 2025, 14(9), 1708; https://doi.org/10.3390/electronics14091708 - 23 Apr 2025

In this paper, a novel tool prototype for harvesting tabletop-grown strawberries is presented. Demonstrating resilience to localization inaccuracies of up to

\pm 15

mm and achieving an average cycle time of

8.02

s at half its maximum operational speed, the tool represents a [...] Read more.

In this paper, a novel tool prototype for harvesting tabletop-grown strawberries is presented. Demonstrating resilience to localization inaccuracies of up to

\pm 15

mm and achieving an average cycle time of

8.02

s at half its maximum operational speed, the tool represents a promising step forward in automating strawberry harvesting. It features a compact 35 mm fruit-engagement width, improving accessibility through its small operational footprint. A complete harvesting system is also proposed that can be mounted to a mobile platform for field tests. An experimental demonstration is performed to showcase the new methodology and derive relevant metrics. Full article

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

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10 pages, 3458 KiB

Open AccessCommunication

Sub-6 GHz GaAs SPDT Switch Co-Designed with Shunt Inductor for ESD Protection

by Jaehyun Kwon, Jaeyong Lee, Jinho Yoo, Taehun Kim and Changkun Park

Electronics 2025, 14(9), 1707; https://doi.org/10.3390/electronics14091707 - 23 Apr 2025

In this study, a single-pole double-throw (SPDT) switch for Sub-6 GHz application is designed. In particular, a shunt inductor is connected to the antenna port of the switch for ESD (electrostatic discharge) protection in RF (radio frequency) front end module. The shunt inductor [...] Read more.

In this study, a single-pole double-throw (SPDT) switch for Sub-6 GHz application is designed. In particular, a shunt inductor is connected to the antenna port of the switch for ESD (electrostatic discharge) protection in RF (radio frequency) front end module. The shunt inductor not only serves as an ESD protection device, but also serves as a component of a parallel resonance circuit to suppress insertion loss of the switch. In addition, in order to secure the power handling capability, transistors turned off in the transmit (Tx) mode are implemented as quadruple-gate transistors. An SPDT switch is fabricated using GaAs pHEMT provided in the 500 nm GaAs BiFET process to verify the feasibility of the proposed switch structure. The operating frequency is set from 3 GHz to 5 GHz. The insertion loss and isolation measured in the Tx mode are lower than 0.35 dB and higher than 31.6 dB, respectively. The insertion loss and isolation measured in the Rx mode are lower than 0.32 dB and higher than 33.9 dB, respectively. The chip size including test pads is 0.890 × 0.875 mm². Full article

(This article belongs to the Special Issue Advances in Design and Applications of RF/MM-Wave Devices and Circuits)

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