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Volume 14
Issue 8
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Electronics, Volume 14, Issue 8 (April-2 2025) – 208 articles

Cover Story (view full-size image): In the following study, an underwater hyperspectral LiDAR (UDHSL) system was designed with a 450–700 nm spectral range, 10–300 nm adjustable bandwidth, and 50 kHz–1 MHz tunable repetition frequency. Featuring ≤1 mrad divergence, 7 ns pulses, and 7.5 µJ energy, the system achieves 1.13 cm ranging resolution and 1.02 m accuracy at a range of 27 m. Experimental 3D mapping demonstrates synchronized spectral-topographic data acquisition through 11-band (450–650 nm) point clouds: distance-colored models reveal target geometry, normalized intensity visualizations enable spectral discrimination, and RGB composites facilitate true-color representation. The system's dual sensing capabilities advance underwater exploration by concurrently resolving the spatial and spectral signatures of submerged targets. View this paper
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25 pages, 8392 KiB  
Article
Assessing Urban Activity and Accessibility in the 20 min City Concept
by Tsetsentsengel Munkhbayar, Zolzaya Dashdorj, Hun-Hee Cho, Jun-Woo Lee, Tae-Koo Kang and Erdenebaatar Altangerel
Electronics 2025, 14(8), 1693; https://doi.org/10.3390/electronics14081693 - 21 Apr 2025
Abstract
The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using [...] Read more.
The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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18 pages, 6047 KiB  
Article
AS-YOLO: A Novel YOLO Model with Multi-Scale Feature Fusion for Intracranial Aneurysm Recognition
by Jun Yang, Chen Wang, Yang Chen, Zhengkui Chen and Jijun Tong
Electronics 2025, 14(8), 1692; https://doi.org/10.3390/electronics14081692 - 21 Apr 2025
Abstract
Intracranial aneurysm is a common clinical disease that seriously endangers the health of patients. In view of the shortcomings of existing intracranial aneurysm recognition methods in dealing with complex aneurysm morphologies, varying sizes, as well as multi-scale feature extraction and lightweight deployment, this [...] Read more.
Intracranial aneurysm is a common clinical disease that seriously endangers the health of patients. In view of the shortcomings of existing intracranial aneurysm recognition methods in dealing with complex aneurysm morphologies, varying sizes, as well as multi-scale feature extraction and lightweight deployment, this study introduces an intracranial aneurysm detection framework, AS-YOLO, which is designed to enhance recognition precision while ensuring compatibility with lightweight device deployment. Built on the YOLOv8n backbone, this approach incorporates a cascaded enhancement module to refine representation learning across scales. In addition, a multi-stage fusion strategy was employed to facilitate efficient integration of cross-scale semantic features. Then, the detection head was improved by proposing an efficient depthwise separable convolutional aggregation detection head. This modification significantly lowers both the parameter count and computational burden without compromising recognition precision. Finally, the SIoU-based regression loss was employed, enhancing the bounding box alignment and boosting overall detection performance. Compared with the original YOLOv8, the proposed solution achieves higher recognition precision for aneurysm detection—boosting mAP@0.5 by 8.7% and mAP@0.5:0.95 by 4.96%. Meanwhile, the overall model complexity is effectively reduced, with a parameter count reduction of 8.21%. Incorporating multi-scale representation fusion and lightweight design, the introduced model maintains high detection accuracy and exhibits strong adaptability in environments with limited computational resources, including mobile health applications. Full article
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24 pages, 10136 KiB  
Article
A Secure Bank Loan Prediction System by Bridging Differential Privacy and Explainable Machine Learning
by Muhammad Minoar Hossain, Mohammad Mamun, Arslan Munir, Mohammad Motiur Rahman and Safiul Haque Chowdhury
Electronics 2025, 14(8), 1691; https://doi.org/10.3390/electronics14081691 - 21 Apr 2025
Abstract
Bank loan prediction (BLP) analyzes the financial records of individuals to conclude possible loan status. Financial records always contain confidential information. Hence, privacy is significant in the BLP system. This research aims to generate a privacy-preserving automated BLP scheme. To achieve this, differential [...] Read more.
Bank loan prediction (BLP) analyzes the financial records of individuals to conclude possible loan status. Financial records always contain confidential information. Hence, privacy is significant in the BLP system. This research aims to generate a privacy-preserving automated BLP scheme. To achieve this, differential privacy (DP) is combined with machine learning (ML). Using a benchmark dataset, the proposed method analyzes two different DP techniques, namely Laplacian and Gaussian, with five different ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Logistic Regression (LR), and Categorical Boosting (CatBoost). Each of the DP techniques is evaluated by varying distinct privacy parameters with 10-fold cross-validation, and from the outcome analysis, optimal parameters are nominated to balance privacy and security. The analysis indicates that applying the Laplacian mechanism with a DP budget of 2 and the RF model achieves the highest accuracy of 62.31%. For the Gaussian method, the best accuracy of 81.25% is attained by the CatBoost model in privacy budget 1.5. Additionally, the proposed method uses explainable artificial intelligence (XAI) to show the conclusion capability of DP-integrated ML models. The proposed research shows an efficient method for automated BLP while preserving the privacy of personal financial information and, thus, mitigating vulnerability to scams and fraud. Full article
(This article belongs to the Special Issue Network Security and Cryptography Applications)
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29 pages, 1326 KiB  
Article
A Coordination Layer for Time Synchronization in Level-4 Multi-vECU Simulation
by Hyeongrae Kim, Harim Lee and Jeonghun Cho
Electronics 2025, 14(8), 1690; https://doi.org/10.3390/electronics14081690 - 21 Apr 2025
Abstract
In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous [...] Read more.
In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous work, we developed a Level-4 vECU using a hardware-level emulator. However, when simulating multiple vECUs with independent clocks across distributed emulators, we observed poor timing reproducibility due to the lack of explicit synchronization. To solve this, we implemented an integration layer compliant with the functional mock-up interface (FMI), a widely used standard for simulation tool interoperability. The layer enables synchronized simulation between a centralized simulation master and independently running vECUs. We also developed a virtual CAN bus model to simulate message arbitration and validate inter-vECU communication behavior. Simulation results show that our framework correctly reproduces CAN arbitration logic and significantly improves timing reproducibility compared to conventional Linux-based interfaces. To improve simulation performance, the FMI master algorithm was parallelized, resulting in up to 85.2% reduction in simulation time with eight vECUs. These contributions offer a practical solution for synchronizing distributed Level-4 vECUs and lay the groundwork for future cloud-native simulation of automotive systems. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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25 pages, 5087 KiB  
Article
Optimizing the Long-Term Efficiency of Users and Operators in Mobile Edge Computing Using Reinforcement Learning
by Jianji Shao and Yanjun Li
Electronics 2025, 14(8), 1689; https://doi.org/10.3390/electronics14081689 - 21 Apr 2025
Abstract
Mobile edge computing (MEC) has emerged as a promising paradigm to enhance computational capabilities at the network edge, enabling low-latency services for users while ensuring efficient resource utilization for operators. One of the key challenges in MEC is optimizing offloading decisions and resource [...] Read more.
Mobile edge computing (MEC) has emerged as a promising paradigm to enhance computational capabilities at the network edge, enabling low-latency services for users while ensuring efficient resource utilization for operators. One of the key challenges in MEC is optimizing offloading decisions and resource allocation to balance user experience and operator profitability. In this paper, we integrate software-defined networking (SDN) and MEC to enhance system utility and propose an SDN-based MEC network framework. Within this framework, we formulate an optimization problem that jointly maximizes the utility of both users and operators by optimizing the offloading decisions, communication and computation resource allocation ratios. To address this challenge, we model the problem as a Markov decision process (MDP) and propose a reinforcement learning (RL)-based algorithm to optimize long-term system utility in a dynamic network environment. However, since RL-based algorithms struggle with large state spaces, we extend the MDP formulation to a continuous state space and develop a deep reinforcement learning (DRL)-based algorithm to improve performance. The DRL approach leverages neural networks to approximate optimal policies, enabling more effective decision-making in complex environments. Experimental results validate the effectiveness of our proposed methods. While the RL-based algorithm enhances the long-term average utility of both users and operators, the DRL-based algorithm further improves performance, increasing operator and user efficiency by approximately 22.4% and 12.2%, respectively. These results highlight the potential of intelligent learning-based approaches for optimizing MEC networks and provide valuable insights into designing adaptive and efficient MEC architectures. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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23 pages, 7011 KiB  
Article
A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification
by Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang and Jiang Li
Electronics 2025, 14(8), 1688; https://doi.org/10.3390/electronics14081688 - 21 Apr 2025
Abstract
Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present [...] Read more.
Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework. Full article
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32 pages, 14073 KiB  
Article
Assertion-Based Verification of I2C Module Using SystemVerilog
by Dae-Won Moon, Seung-Hyun Pyo, Dae-Ki Hong, Otgonbayar Bataa and Erdenekhuu Norinpel
Electronics 2025, 14(8), 1687; https://doi.org/10.3390/electronics14081687 - 21 Apr 2025
Abstract
In today’s semiconductor verification field, SystemVerilog Assertions (SVAs) are one of the most important methodologies for functional verification. A representative verification technique is Universal Verification Methodology (UVM)-based verification, which utilizes a SystemVerilog class library. On the other hand, Assertion-Based Verification (ABV) using SVA [...] Read more.
In today’s semiconductor verification field, SystemVerilog Assertions (SVAs) are one of the most important methodologies for functional verification. A representative verification technique is Universal Verification Methodology (UVM)-based verification, which utilizes a SystemVerilog class library. On the other hand, Assertion-Based Verification (ABV) using SVA allows hardware designs to be verified without requiring knowledge of SystemVerilog’s Object-Oriented Programming (OOP) concepts or UVM. Its syntax is intuitive and concise, enabling rapid detection of RTL (Register Transfer Level) bugs during the design and verification phases. This methodology significantly enhances productivity by drastically reducing the time required for semiconductor development. This paper proposes an ABV verification environment using SVA that is reusable in other verification environments for verifying the main functions of the Inter-Integrated Circuit (I2C), one of the serial synchronous communication protocols. Finally, through the development of the RTL design and simulation of the core functionalities of I2C, the key characteristics of I2C were verified using ABV with SVA. Full article
(This article belongs to the Section Computer Science & Engineering)
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23 pages, 4323 KiB  
Article
Network Function Placement in Virtualized Radio Access Network with Reinforcement Learning Based on Graph Neural Network
by Mengting Yi, Mugang Lin and Wenhui Chen
Electronics 2025, 14(8), 1686; https://doi.org/10.3390/electronics14081686 - 21 Apr 2025
Abstract
In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement [...] Read more.
In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement problem is known to be NP-hard, and previous studies have attempted to address it using Deep Reinforcement Learning (DRL) approaches. Nevertheless, many existing methods fail to capture the network state in RANs with specific topologies, leading to suboptimal decision-making and resource allocation. In this paper, we propose a method referred to as GDRL, which is a deep reinforcement learning approach that utilizes graph neural networks to address the functional placement problem. To ensure policy stability, we design a policy gradient algorithm called Graph Proximal Policy Optimization (GPPO), which integrates GNNs into both the actor and critic networks. By incorporating both node and edge features, the GDRL enhances feature extraction from the RAN’s nodes and links, providing richer observational data for decision-making and evaluation. This, in turn, enables more accurate and effective decision outcomes. In addition, we formulate the problem as a mixed-integer nonlinear programming model aimed at minimizing the number of active computational nodes while maximizing the centralization level of the virtualized RAN (vRAN). We evaluate the GDRL across different RAN scenarios with varying node configurations. The results demonstrate that our approach achieves superior network centralization and outperforms several existing methods in overall performance. Full article
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26 pages, 7368 KiB  
Article
Latency-Aware and Auto-Migrating Page Tables for ARM NUMA Servers
by Hongliang Qu and Peng Wang
Electronics 2025, 14(8), 1685; https://doi.org/10.3390/electronics14081685 - 21 Apr 2025
Abstract
The non-uniform memory access (NUMA) architecture is the de facto norm in modern server processors. Applications running on NUMA processors may suffer significant performance degradation (NUMA effect) due to the non-uniform memory accesses, including data and page table accesses. Recent studies show that [...] Read more.
The non-uniform memory access (NUMA) architecture is the de facto norm in modern server processors. Applications running on NUMA processors may suffer significant performance degradation (NUMA effect) due to the non-uniform memory accesses, including data and page table accesses. Recent studies show that the NUMA effect of long-running memory-intensive workloads can be mitigated by replicating or migrating page tables to nodes that require accesses to remote page tables. However, this technique cannot adapt to the situation where other applications compete for the memory controller. Furthermore, it was only implemented on x86 processors and cannot be readily applied on ARM server processors, which are becoming increasingly popular. To address this issue, we designed the page table access latency aware (PTL-aware) page table auto-migration (Auto-PTM) mechanism. Then we implemented it on Linux ARM64 (the Linux kernel name for AArch64) by identifying the differences between the ARM architecture and the x86 architecture in terms of page table structure and the implementation of the Linux kernel source code. We evaluate it on real ARM NUMA servers. The experimental results demonstrate that, compared to the state-of-the-art PTM mechanism, our PTL-aware mechanism significantly enhances the performance of workloads in various scenarios (e.g., GUPS by 3.53x, XSBench by 1.77x, Hashjoin by 1.68x). Full article
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37 pages, 1159 KiB  
Review
Cyber–Physical Resilience: Evolution of Concept, Indicators, and Legal Frameworks
by Antonella Longo, Ali Aghazadeh Ardebili, Alessandro Lazari and Antonio Ficarella
Electronics 2025, 14(8), 1684; https://doi.org/10.3390/electronics14081684 - 21 Apr 2025
Abstract
The protection of critical infrastructures (CIs) from cyber–physical threats and natural hazards has become increasingly vital in modern society, which relies heavily on the essential services provided by these infrastructures. The European Union has emphasized the importance of this issue by deploying a [...] Read more.
The protection of critical infrastructures (CIs) from cyber–physical threats and natural hazards has become increasingly vital in modern society, which relies heavily on the essential services provided by these infrastructures. The European Union has emphasized the importance of this issue by deploying a comprehensive policy package in 2022, including the NIS2 and CER Directives. This paper explores the concept of resilience in critical entities and essential services from a cyber–physical perspective. It addresses the inherent complexity of CIs and discusses challenges, limitations, and future research directions for enhancing their protection in line with EU policies. Furthermore, it introduces a conceptual model of resilience, outlining its analytical dimensions, and reviews current resilience indicators and corresponding assessment frameworks. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Recent Developments and Emerging Trends)
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17 pages, 18396 KiB  
Article
SSA-VMD-Double-Fuzzy-Logic for Human Vital Signs Detection Using a UWB Radar
by Ji Li, Weixin Zhang, Zeping Xu, Yunpeng Wang, Zhaotian Deng, Chengwu You and Chengpei Tang
Electronics 2025, 14(8), 1683; https://doi.org/10.3390/electronics14081683 - 21 Apr 2025
Abstract
Ultra-wide-band (UWB) radar technology is a contactless signal detection technology. The UWB technology can be used for the clinical medical monitoring of the human heartbeat and respiration. In this paper, a novel algorithm is proposed to estimate heart rate (HR) and respiratory rate [...] Read more.
Ultra-wide-band (UWB) radar technology is a contactless signal detection technology. The UWB technology can be used for the clinical medical monitoring of the human heartbeat and respiration. In this paper, a novel algorithm is proposed to estimate heart rate (HR) and respiratory rate (RR) depending on the received UWB radar signal. The proposed algorithm initially applies Singular Spectrum Analysis (SSA) to remove noise from the received UWB radar signal, and then applies Variational Mode Decomposition (VMD) to effectively separate the respiratory and heartbeat components. To address the issue of heartbeat components being susceptible to harmonic and inter-modulation components interference, a double fuzzy logic estimation is implemented to achieve robust real-time extraction of heart rate. In this paper, extensive experiments are conducted at various distances and angles. The experimental results of the SSA-VMD-Double-Fuzzy-Logic (SVDF) method have been compared with other methods, demonstrating its effectiveness and the advantages of the SVDF proposed in this paper. Full article
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19 pages, 2300 KiB  
Article
Beyond Isolated Features: Group-Level Feature-Driven Multimodal Fusion for Entity Relationship Extraction
by Yana Lv, Jiaqi Tao and Xiuli Du
Electronics 2025, 14(8), 1682; https://doi.org/10.3390/electronics14081682 - 21 Apr 2025
Abstract
Named entity recognition and relation extraction are two crucial techniques in the construction of knowledge graphs, as their performance directly impacts downstream tasks. In scenarios such as social media, where text is short and contains numerous references, relying solely on a single modality [...] Read more.
Named entity recognition and relation extraction are two crucial techniques in the construction of knowledge graphs, as their performance directly impacts downstream tasks. In scenarios such as social media, where text is short and contains numerous references, relying solely on a single modality often leads to suboptimal entity-relation extraction results. To address this issue, this paper proposes a multimodal entity-relation extraction model. The model incorporates group-level features into visual representations and integrates them into a BERT variant using a dynamic gating mechanism and an attention mechanism. This approach effectively reduces modal noise, balances the gap between modalities to some extent, and enhances both encoding performance and data utilization. The experimental results demonstrate that the proposed model achieves F1 scores of 87.78% and 84.16% on public datasets for MNER and MRE tasks, respectively, outperforming baseline models. Additionally, ablation studies validate the effectiveness of each module within the proposed model. Full article
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11 pages, 921 KiB  
Article
A Physiological Evaluation of Driver Workload in the Lead Vehicle of an Autonomous Truck Platoon Using Bio-Signal Analysis
by Emi Yuda, Junichiro Hayano and Makoto Takahashi
Electronics 2025, 14(8), 1681; https://doi.org/10.3390/electronics14081681 - 21 Apr 2025
Abstract
The evaluation of driver workload in the lead vehicle of a driver-following autonomous truck platoon was conducted using bio-signal analysis. In this study, a single driver operated the lead vehicle while the second and third trucks followed autonomously. Three professional truck drivers (38 [...] Read more.
The evaluation of driver workload in the lead vehicle of a driver-following autonomous truck platoon was conducted using bio-signal analysis. In this study, a single driver operated the lead vehicle while the second and third trucks followed autonomously. Three professional truck drivers (38 ± 4 years old, male) participated in the experiment. During driving, wearable sensors measured heart-rate variability indices, body acceleration, and skin temperature. The heart rate and body acceleration were sampled at 128 Hz (7.8 ms intervals), while skin temperature was recorded at 1 Hz. Each participant underwent three measurement sessions on different days, with each session lasting approximately 30–40 min. Statistical analysis was performed using repeated-measures ANOVA to determine significant differences across conditions and days. The results indicated that compared to solo driving, driving the lead vehicle of the autonomous platoon significantly increased skin temperature (p < 0.001), suggesting a higher physiological workload. This study provides insight into the physiological impact of autonomous platooning on lead-vehicle drivers, which is crucial for developing strategies to mitigate driver workload in such systems. Full article
(This article belongs to the Special Issue New Application of Wearable Electronics)
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25 pages, 3771 KiB  
Article
RBFAC: A Redactable Blockchain Framework with Fine-Grained Access Control Based on Flexible Policy Chameleon Hash
by Shiyang Wu, Lifei Wei, Shihai Wu and Lei Zhang
Electronics 2025, 14(8), 1680; https://doi.org/10.3390/electronics14081680 - 21 Apr 2025
Abstract
While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, [...] Read more.
While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, current redactable blockchain implementations exhibit significant limitations, particularly in their inability to separate data editing from policy modification and their insufficient support for decentralized management of diverse editing operations. To address these issues, this paper initially introduces the concept of Flexible Policy Chameleon Hash (FPCH), which integrates PCH with non-interactive zero-knowledge proofs to enable enhanced policy management flexibility. Moreover, this paper proposes a Redactable Blockchain Framework with Fine-grained Access Control (RBFAC) based on FPCH. The RBFAC framework employs a hybrid cryptographic approach to separate the right of data editing from policy modification. The framework also provides essential functionalities, including editing accountability, key tracking and revocation mechanisms, and policy privacy protection. Finally, experimental evaluations demonstrate that the RBFAC framework maintains acceptable performance overhead while delivering these advanced features. The results indicate that the proposed solution addresses the limitations of existing redactable blockchain systems, offering a more flexible and secure approach to controlled data editing in blockchain environments. Full article
(This article belongs to the Special Issue Applied Cryptography and Practical Cryptoanalysis for Web 3.0)
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25 pages, 6066 KiB  
Article
CNN-Based Fault Classification in Induction Motors Using Feature Vector Images of Symmetrical Components
by Tae-Hong Min, Joong-Hyeok Lee and Byeong-Keun Choi
Electronics 2025, 14(8), 1679; https://doi.org/10.3390/electronics14081679 - 21 Apr 2025
Abstract
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on [...] Read more.
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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23 pages, 2111 KiB  
Article
GAShellBreaker: A Novel Method for Java Fileless Webshell Detection Based on Grayscale Images and Deep Learning
by Yuan Zhang, Daofeng Li and Yuqin Xie
Electronics 2025, 14(8), 1678; https://doi.org/10.3390/electronics14081678 - 21 Apr 2025
Abstract
Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers are shifting toward fileless Webshells, which reside entirely in memory and present significant challenges to conventional [...] Read more.
Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers are shifting toward fileless Webshells, which reside entirely in memory and present significant challenges to conventional security tools. However, research on fileless Webshell detection remains limited. To address this gap, we analyzed various fileless Webshell samples, summarized their behavioral patterns, and constructed a corresponding threat model. Based on this, we propose a novel detection approach named GAShellBreaker, which leverages grayscale image transformation and deep learning. GAShellBreaker first establishes a dual-layer in-memory monitoring mechanism to capture suspicious classes within the Java Virtual Machine (JVM) and export them as bytecode files. It then extracts opcode sequences from these files, transforms them into grayscale images, and employs a ResNet50-based classifier for detection. Due to the limited availability of fileless samples, we trained and evaluated the model on a larger dataset of 1351 file-based scripts (383 Webshells and 968 benign samples), and used 56 fileless Webshells for validation. Experimental results show that GAShellBreaker achieves 99.10% accuracy on file-based Webshells and 89.29% accuracy on fileless Webshells, outperforming existing algorithms. Moreover, it maintains low computational overhead (6.7%), confirming its practical feasibility. Full article
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7 pages, 177 KiB  
Editorial
Advanced Research in Technology and Information Systems
by Vasile-Daniel Pavaloaia, Rodrigo Martin-Rojas and Piotr Sulikowski
Electronics 2025, 14(8), 1677; https://doi.org/10.3390/electronics14081677 - 21 Apr 2025
Abstract
The fields of information systems and technology continue to evolve at a rapid pace, offering new research directions and expanding career opportunities [...] Full article
(This article belongs to the Special Issue Advanced Research in Technology and Information Systems)
39 pages, 3508 KiB  
Article
IV-Nlp: A Methodology to Understand the Behavior of DL Models and Its Application from a Causal Approach
by Yudi Guzman-Monteza, Juan M. Fernandez-Luna and Francisco J. Ribadas-Pena
Electronics 2025, 14(8), 1676; https://doi.org/10.3390/electronics14081676 - 21 Apr 2025
Abstract
Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its application. IV-NLP integrates two approaches. The first defines the [...] Read more.
Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its application. IV-NLP integrates two approaches. The first defines the process of the inference and estimation of the causal effect in original, predicted, and synthetic data. The second one includes a validation method of the results obtained by the selected Large-Language Model (LLM). IV-NLP proposes to use synthetic data in predictive tasks only if the causal effect pattern of the synthetic data is aligned with the causal effect pattern of the original data. DL models, the Instrumental Variable (IV) method, statistical methods, and GPT-3.5-turbo-0125 were used for its application, including an intervention method using a variation of the Retrieval-Augmented Generation (RAG) technique. Our findings reveal notable discrepancies between the original and synthetic data, highlighting that the synthetic data do not fully capture the underlying causal effect patterns of the original data, evidencing homogeneity and low diversity in the synthetic data. Interestingly, when evaluating the causal effect in the predictions made by our three best DL models, it was verified that the model with the lowest accuracy (84.50%) was fully aligned with the overall causal effect pattern. These results demonstrate the potential of integrating DL and LLM models with causal inference methods. Full article
(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)
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22 pages, 4505 KiB  
Article
Advancing Secret Sharing in 3D Models Through Vertex Index Sharing
by Yuan-Yu Tsai, Jyun-Yu Jhou, Tz-Yi You and Ching-Ta Lu
Electronics 2025, 14(8), 1675; https://doi.org/10.3390/electronics14081675 - 21 Apr 2025
Abstract
Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in [...] Read more.
Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in the realm of images, its exploration and application in 3D models remain in their early stages. Given the growing prominence of 3D models in multimedia applications, ensuring their security and privacy has emerged as a critical area of research. At present, secret sharing approaches for 3D models predominantly rely on the vertex coordinates of the model as the basis for embedding and reconstructing secret messages. However, due to the limited quantity of vertex coordinates, these methods face significant constraints in embedding capacity, thereby limiting the potential of 3D models in secure data sharing. In contrast, the vertex indices of polygons, characterized by higher information density and greater structural flexibility, present a promising alternative medium for embedding secret shares. Building on this premise, the present study investigates the feasibility of leveraging shared vertex indices as a foundation for message embedding. It highlights the advantages of this approach in enhancing both the embedding capacity and the overall security of 3D models. By integrating the Chinese Remainder Theorem into vertex index-based sharing, the proposed method strengthens existing algorithms, offering improved model protection and enhanced embedding security. Experimental evaluations reveal that, compared to traditional vertex coordinate-based methods, incorporating vertex indices into secret sharing techniques significantly increases embedding efficiency while bolstering the security of 3D models. This study not only introduces an innovative approach to safeguarding 3D model data but also paves the way for the broader application of secret sharing techniques in the future. Full article
(This article belongs to the Special Issue Advancements in Network and Data Security)
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19 pages, 1090 KiB  
Article
TeeDFuzzer: Fuzzing Trusted Execution Environment
by Sheng Wen, Liam Xu, Liwei Tian, Suping Liu and Yong Ding
Electronics 2025, 14(8), 1674; https://doi.org/10.3390/electronics14081674 - 21 Apr 2025
Abstract
The Trusted Execution Environment (TEE) is crucial for safeguarding the ecosystem of embedded systems. It uses isolation to minimize the TCB (Trusted Computing Base) and protect sensitive software. It is vital because devices handle vast, potentially sensitive data. Leveraging ARM TrustZone, widely used [...] Read more.
The Trusted Execution Environment (TEE) is crucial for safeguarding the ecosystem of embedded systems. It uses isolation to minimize the TCB (Trusted Computing Base) and protect sensitive software. It is vital because devices handle vast, potentially sensitive data. Leveraging ARM TrustZone, widely used in mobile and IoT for TEEs, it ensures hardware protection via security extensions, though needing firmware and software stack support. Despite the reputation of TEEs for high security, TrustZone-aided ones have vulnerabilities. Fuzzing, as a practical bug-finding technique, has seen limited research in the context of TEE. The unique software architecture of TrustZone-assisted TEE complicates the direct application of traditional fuzzing methods. Moreover, simplistic approaches, such as feeding random input values into TEE through the API functions of the rich operating system, fail to uncover deeper, latent bugs within the TEE code. In this paper, we present a fuzzing strategy for TrustZone-assisted TEE that utilizes inferred dependencies between Trusted Kernel system calls to uncover deep-seated TEE bugs. We implemented our approach on OP-TEE, where it successfully identified 17 crashes, including one previously undetected kernel bug. Full article
(This article belongs to the Special Issue Advances in Software Engineering and Programming Languages)
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14 pages, 4120 KiB  
Article
SACG-YOLO: A Method of Transmission Line Insulator Defect Detection by Fusing Scene-Aware Information and Detailed-Content-Guided Information
by Lihui Zhao, Jun Kang, Yang An, Yurong Li, Meili Jia and Ruihong Li
Electronics 2025, 14(8), 1673; https://doi.org/10.3390/electronics14081673 - 20 Apr 2025
Abstract
To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module [...] Read more.
To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module is employed to extract global environmental information, improving the baseline model’s adaptability to complex backgrounds. Second, a detailed content attention module is introduced to enable the model to more accurately capture fine-grained features of small defect regions. Furthermore, a normalized Wasserstein distance metric function is adopted to mitigate the sensitivity of the regression branch in the baseline model. Simultaneously, a sample weighting function is utilized to reduce the impact of sample imbalance on the classification branch. Experimental results demonstrate that the proposed method achieves superior detection performance on a real-world transmission line insulator defect dataset. Full article
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23 pages, 9237 KiB  
Article
Design and Optimization of an Internet of Things-Based Cloud Platform for Autonomous Agricultural Machinery Using Narrowband Internet of Things and 5G Dual-Channel Communication
by Baidong Zhao, Dingkun Zheng, Chenghan Yang, Shuang Wang, Madina Mansurova, Sholpan Jomartova, Nadezhda Kunicina, Anatolijs Zabasta, Vladimir Beliaev, Jelena Caiko and Roberts Grants
Electronics 2025, 14(8), 1672; https://doi.org/10.3390/electronics14081672 - 20 Apr 2025
Abstract
This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and [...] Read more.
This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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22 pages, 7958 KiB  
Article
Depth Upsampling with Local and Nonlocal Models Using Adaptive Bandwidth
by Niloufar Salehi Dastjerdi and M. Omair Ahmad
Electronics 2025, 14(8), 1671; https://doi.org/10.3390/electronics14081671 - 20 Apr 2025
Abstract
The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. [...] Read more.
The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article
(This article belongs to the Special Issue Image and Video Processing for Emerging Multimedia Technology)
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21 pages, 13056 KiB  
Article
Package Integration and System Performance Analysis of Glass-Based Passive Components for 5G New Radio Millimeter-Wave Modules
by Muhammad Ali, Atom Watanabe, Takenori Kakutani, Pulugurtha M. Raj, Rao. R. Tummala and Madhavan Swaminathan
Electronics 2025, 14(8), 1670; https://doi.org/10.3390/electronics14081670 - 20 Apr 2025
Abstract
In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and [...] Read more.
In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and n260 are modeled, designed, fabricated and characterized individually along with their integrated versions. Non–contiguous diplexers are designed using three different types of filters, hairpin, interdigital and edge–coupled, and combined with a broadband coupler to emulate a power detection and control circuitry block in an RF transmitter chain. A panel–compatible semi–additive patterning (SAP) process is utilized to form high–precision redistribution layers (RDLs) on laminated glass substrate, onto which fine features with tight tolerance are added to fabricate these structures. The diplexers exhibit low insertion loss, low VSWR and high isolation, and have a small footprint. A system performance analysis using a co–simulation technique is presented for the first time to quantify the distortion in amplitude and phase produced by the fabricated passive component block in terms of error vector magnitude (EVM). Moreover, the scalability of this approach to compare similar passive components based on their specifications and signatures using a system–level performance metric such as EVM is discussed. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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19 pages, 12455 KiB  
Article
Research on Mobile Robot Path Planning Based on an Improved Bidirectional Jump Point Search Algorithm
by Rui Guo, Xingbo Quan and Changchun Bao
Electronics 2025, 14(8), 1669; https://doi.org/10.3390/electronics14081669 - 20 Apr 2025
Abstract
This study proposes an improved bidirectional dynamic jump point search (JPS) algorithm to address key challenges in mobile robot path planning, including excessive node expansions, poor path smoothness, safety concerns, and extended search times. The core novelty of this algorithm lies in the [...] Read more.
This study proposes an improved bidirectional dynamic jump point search (JPS) algorithm to address key challenges in mobile robot path planning, including excessive node expansions, poor path smoothness, safety concerns, and extended search times. The core novelty of this algorithm lies in the introduction of adaptive weight coefficients in the heuristic function and dynamic constraint circles to optimize node expansions. Specifically, the adaptive heuristic function dynamically adjusts the weight coefficients based on the current position relative to the target point, significantly accelerating path searches while ensuring accuracy. Additionally, a dynamically constrained circle is introduced, which defines an adaptive search region, prioritizing node expansions within its boundary and effectively reducing unnecessary searches. Moreover, the jump point selection rules have been optimized to eliminate hazardous nodes and further improve path safety and practicality. Simulation tests conducted on grid maps with varying complexities clearly demonstrate that the proposed algorithm considerably reduces search times by up to 66.27% compared with conventional A*, traditional JPS, and bidirectional JPS methods. Finally, physical mobile robot experiments further validate the effectiveness and real-world applicability of the proposed algorithm. Full article
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19 pages, 8428 KiB  
Article
Cascadable Complementary SSF-Based Biquads with 8 GHz Cutoff Frequency and Very Low Power Consumption
by Matteo Lombardo, Francesco Centurelli, Pietro Monsurrò and Alessandro Trifiletti
Electronics 2025, 14(8), 1668; https://doi.org/10.3390/electronics14081668 - 20 Apr 2025
Abstract
Low-pass filters with bandwidths larger than several GHz are required in many applications, such as anti-aliasing filters in high-speed ADCs and pulse-shaping filters in high-speed DACs. In highly integrated applications, low area occupation and power consumption are key specifications, so inductor-less implementations are [...] Read more.
Low-pass filters with bandwidths larger than several GHz are required in many applications, such as anti-aliasing filters in high-speed ADCs and pulse-shaping filters in high-speed DACs. In highly integrated applications, low area occupation and power consumption are key specifications, so inductor-less implementations are to be preferred. Furthermore, full CMOS implementations provide an advantage in terms of technology availability and cost. In this paper, we present an inductor-less CMOS biquad stage based on the super source follower topology that provides an 8 GHz cutoff frequency and a low power consumption of 0.42 mW per pole, showing remarkable performance also in terms of bandwidth and dynamic range. The availability of two separate current sources allows independent tuning of natural frequency and quality factor. The stage can be implemented in two complementary ways, exploiting NMOS and PMOS input devices, respectively, thus simplifying cascadability. The two complementary biquads have been implemented in the STMicroelectronics FDSOI 28 nm CMOS process and extensively simulated and provide stable performance under PVT variations and mismatches. The area occupation is about 387.5 μm2 per biquad, one of the lowest in the literature. The figures-of-merit are remarkable, as the filters achieve excellent power efficiency, very low area occupation, and good dynamic range. Full article
(This article belongs to the Special Issue Advances in RF, Analog, and Mixed Signal Circuits)
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19 pages, 4401 KiB  
Article
An Integrated RF Sensor Design for Anchor-Free Collaborative Localization in GNSS-Denied Environments
by Di Bai, Xinran Li, Lingyun Zhou, Chunyong Yang, Yongqiang Cui, Liyun Bai and Yunhao Chen
Electronics 2025, 14(8), 1667; https://doi.org/10.3390/electronics14081667 - 20 Apr 2025
Abstract
To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor [...] Read more.
To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor estimates inter-node distances and orientations using wireless measurements between nodes, without requiring pre-deployed anchor points. First, we designed a low-nanosecond latency ranging logic circuit on field-programmable gate array (FPGA) hardware, enabling relative distance estimation between nodes via a low-latency collaborative ranging (LLCR) algorithm without synchronization. Additionally, a synthetic aperture rotating antenna system was built to construct an echo space energy distribution matrix, based on dynamic–static dual-channel phase differences for high-precision, unambiguous azimuth measurement, followed by angle and distance data integration for localization. Then, a novel RF sensor hardware system was designed that was lightweight, low in cost, and high in performance. Finally, two generations of prototype models were developed and tested in both an anechoic chamber and mounted on unmanned vehicles outdoors in fields. The results demonstrate that the proposed sensor can achieve high-precision relative position estimation between collaborative nodes in the absence of GNSS, with a positioning error of within 0.4 m, indicating that it is suitable for mounting on unmanned vehicles and other autonomous systems for collaborative positioning. Full article
(This article belongs to the Special Issue Enhancing Security, Reliability and Service Quality for IoT-Based Systems)
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12 pages, 1258 KiB  
Article
Efficient Multiple-Input–Multiple-Output Channel State Information Feedback: A Semantic-Knowledge-Base- Driven Approach
by Ling Tang, Yaping Sun, Shumin Yao, Xiaodong Xu, Hao Chen and Zhiyong Luo
Electronics 2025, 14(8), 1666; https://doi.org/10.3390/electronics14081666 - 20 Apr 2025
Abstract
Massive multiple-input–multiple-output (MIMO) systems encounter substantial challenges in relation to channel state information (CSI) feedback; this is particularly the case in frequency division duplex systems, where the lack of channel reciprocity necessitates high-dimensional CSI transmission, resulting in substantial feedback overheads. Most existing deep [...] Read more.
Massive multiple-input–multiple-output (MIMO) systems encounter substantial challenges in relation to channel state information (CSI) feedback; this is particularly the case in frequency division duplex systems, where the lack of channel reciprocity necessitates high-dimensional CSI transmission, resulting in substantial feedback overheads. Most existing deep learning methods have improved compression efficiency but still suffer from high feedback overheads due to their transmission of entire compressed vectors. To address this, we propose SKBNet, an innovative semantic knowledge base (SKB)-driven framework for efficient MIMO CSI feedback. By sharing the SKB between the transmitter and receiver, SKBNet transmits only index values, significantly reducing feedback overheads while achieving efficient CSI compression and accurate reconstruction. The simulation results demonstrate that SKBNet outperforms many existing methods in normalized mean square error, cosine similarity, and feedback bit-count at various compression ratios. This framework offers a promising solution for low-overhead, high-precision CSI feedback for future semantic communication networks. Full article
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21 pages, 2432 KiB  
Article
Research on Consensus Algorithm for Intellectual Property Transactions Based on Practical Byzantine Fault Tolerant (PBFT) Algorithm
by Dan Du, Wenlong Feng, Mengxing Huang, Siling Feng and Jing Wang
Electronics 2025, 14(8), 1665; https://doi.org/10.3390/electronics14081665 - 20 Apr 2025
Abstract
Aiming at the problems of significant communication overheads, the low reliability of primary nodes, and the insufficient dynamic adaptability of traditional consensus algorithms in intellectual property transaction scenarios, an Improved Practical Byzantine Fault Tolerant (IPBFT) algorithm based on the Chord algorithm and entropy [...] Read more.
Aiming at the problems of significant communication overheads, the low reliability of primary nodes, and the insufficient dynamic adaptability of traditional consensus algorithms in intellectual property transaction scenarios, an Improved Practical Byzantine Fault Tolerant (IPBFT) algorithm based on the Chord algorithm and entropy weight method is proposed. Firstly, the Chord algorithm is employed to map nodes onto a hash ring, enabling dynamic grouping. Secondly, an entropy-based dynamic reputation model is constructed, quantifying the evaluation of node behaviors and calculating the overall reputation value. A three-level reputation classification mechanism is used to dynamically select primary and supervisory nodes, thereby reducing the probability of Byzantine nodes being elected. Then, a three-phase monitoring strategy for supervisory nodes is developed, which includes collection, review, and blackout. This improves the Raft consensus process, enhancing the detection and fault tolerance against malicious leaders. Finally, a grouped dual-layer consensus architecture is proposed. The lower layer uses an improved Raft algorithm for efficient consensus within groups, while the upper layer uses the PBFT algorithm for cross-group global consistency verification. Experimental findings demonstrate that the IPBFT algorithm is able to balance security, scalability, and consensus efficiency in a dynamic network environment, providing a better consensus solution for intellectual property transactions. Full article
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14 pages, 3318 KiB  
Article
An Adaptive Signal Control Model for Intersection Based on Deep Reinforcement Learning Considering Carbon Emissions
by Lin Duan and Hongxing Zhao
Electronics 2025, 14(8), 1664; https://doi.org/10.3390/electronics14081664 - 20 Apr 2025
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Abstract
To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic [...] Read more.
To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO2 emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO2 emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO2 emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Advances in Object Detection and Traffic Management)
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