Electronics

4228 KiB

Open AccessArticle

Combining the Viterbi Algorithm and Graph Neural Networks for Efficient MIMO Detection

by Thien An Nguyen, Xuan-Toan Dang, Oh-Soon Shin and Jaejin Lee

Electronics 2025, 14(9), 1698; https://doi.org/10.3390/electronics14091698 (registering DOI) - 22 Apr 2025

In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate [...] Read more.

In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate interference and enhance the desired signal. Mathematically, these algorithms operate as linear transformations or functions of received signals. To further enhance MIMO detection performance, researchers have explored the use of nonlinear transformations and functions by leveraging deep learning structures and models. In this paper, we propose a novel model that integrates the Viterbi algorithm with a graph neural network (GNN) to improve signal detection in MIMO systems. Our approach begins by detecting the received signal using the VA, whose output serves as the initial input for the GNN model. Within the GNN framework, the initial signal and the received signal are represented as nodes, while the MIMO channel structure defines the edges. Through an iterative message-passing mechanism, the GNN progressively refines the initial signal, enhancing its accuracy to better approximate the originally transmitted signal. Experimental results demonstrate that the proposed model outperforms conventional and existing approaches, leading to superior detection performance. Full article

(This article belongs to the Special Issue New Trends in Next-Generation Wireless Transmissions)

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54664 KiB

Open AccessArticle

Lensless Digital Holographic Reconstruction Based on the Deep Unfolding Iterative Shrinkage Thresholding Network

by Duofang Chen, Zijian Guo, Huidi Guan and Xueli Chen

Electronics 2025, 14(9), 1697; https://doi.org/10.3390/electronics14091697 (registering DOI) - 22 Apr 2025

Abstract

Without using any optical lenses, lensless digital holography (LDH) records the hologram of a sample and numerically retrieves the amplitude and phase of the sample from the hologram. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable, [...] Read more.

Without using any optical lenses, lensless digital holography (LDH) records the hologram of a sample and numerically retrieves the amplitude and phase of the sample from the hologram. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable, and cost-effective devices to potentially address various point-of-care-, global health-, and telemedicine-related challenges. However, in lensless digital holography, the reconstruction results are severely affected by zero-order noise and twin images as only the hologram intensity can be recorded. To mitigate such interference and enhance image quality, extensive efforts have been made. In recent years, deep learning (DL)-based approaches have made significant advancements in the field of LDH reconstruction. It is well known that most deep learning networks are often regarded as black-box models, which poses challenges in terms of interpretability. Here, we present a deep unfolding network, dubbed the ISTAHolo-Net, for LDH reconstruction. The ISTAHolo-Net replaces the traditional iterative update steps with a fixed number of sub-networks and the regularization weights with learnable parameters. Every sub-network consists of two modules, which are the gradient descent module (GDM) and the proximal mapping module (PMM), respectively. The ISTAHolo-Net incorporates the sparsity-constrained inverse problem model into the neural network and hence combines the interpretability of traditional iterative algorithms with the learning capabilities of neural networks. Simulation and real experiments were conducted to verify the effectiveness of the proposed reconstruction method. The performance of the proposed method was compared with the angular spectrum method (ASM), the HRNet, the Y-Net, and the DH-GAN. The results show that the DL-based reconstruction algorithms can effectively reduce the interference of twin images, thereby improving image reconstruction quality, and the proposed ISTAHolo-Net performs best on our dataset. Full article

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8895 KiB

Open AccessArticle

A Black Start Recovery Strategy for a PV-Based Energy Storage Microgrid, Considering the State of Charge of Energy Storage

by Xiaoyu Li, Tianxiang Ma, Zhiyuan Zhang, Da Zhang, Yan Xu and Kaichen Wang

Electronics 2025, 14(9), 1696; https://doi.org/10.3390/electronics14091696 (registering DOI) - 22 Apr 2025

Abstract

To mitigate black start failures resulting from energy storage state of charge (SOC) exceeding operational limits, this study develops a restoration strategy incorporating SOC constraints. Firstly, an adaptive SOC control without bias for energy storage units is proposed to achieve SOC balance. Secondly, [...] Read more.

To mitigate black start failures resulting from energy storage state of charge (SOC) exceeding operational limits, this study develops a restoration strategy incorporating SOC constraints. Firstly, an adaptive SOC control without bias for energy storage units is proposed to achieve SOC balance. Secondly, the maximum power point tracking (MPPT) mode for photovoltaic power generation is integrated with the load tracking mode, enabling effective tracking of load variations when the photovoltaic output is sufficient; conversely, when the photovoltaic output is inadequate, the energy storage output compensates for the shortfall, thus avoiding the SOC limit due to an insufficient remaining SOC of the energy storage, while also significantly reducing the quantity of charge and discharge cycles undergone by the energy storage units. Finally, the simulation results show that, according to the proposed control strategy for recovery, the maximum system frequency of the black start process does not exceed 50.11, and the minimum is not lower than 49.82, which are within reasonable limits. At the same time, the number of charge/discharge conversions of the three storage batteries is 12 when the PV system adopts the coordinated control of MPPT and load tracking, and the number of charge/discharge conversions of the storage batteries in the PV MPPT mode is 21. This ensures the success of the black start process and prolongs the life of the energy storage battery. Full article

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973 KiB

Open AccessArticle

Compensator with Weighted Input Parameters for Automatic Ball-Balancing Mechatronic System

by Ján Šefčík, Štefan Chamraz and Katarína Žáková

Electronics 2025, 14(9), 1695; https://doi.org/10.3390/electronics14091695 (registering DOI) - 22 Apr 2025

Abstract

This paper presents an Automatic Ball-Balancing Mechatronic System (ABMS) with a lever transmission, which provides higher positioning accuracy for the ball. The system was identified by a double integrator, and the results confirmed the suitability of the chosen mathematical model. Then, we designed [...] Read more.

This paper presents an Automatic Ball-Balancing Mechatronic System (ABMS) with a lever transmission, which provides higher positioning accuracy for the ball. The system was identified by a double integrator, and the results confirmed the suitability of the chosen mathematical model. Then, we designed and tested a new compensator with weighted input parameters, which was firstly successfully implemented in real time on a microprocessor platform. Both simulation and then also experimental results demonstrated that the proposed controller provides stable and accurate control of the system under various step changes in the input signal. Full article

(This article belongs to the Special Issue Advanced PID Control: Theory and Applications Towards a Smarter PID Controller)

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2267 KiB

Open AccessReview

A Review of Battery Electric Public Transport Timetabling and Scheduling: A 10 Year Retrospective and New Developments

by Yaoyao Wang, Shun Zhang, Liang Liu, Ping Gong, Weike Lu, Fuwei Wu, Jinggang Gu, Yuxuan Li and Zhichao Cao

Electronics 2025, 14(9), 1694; https://doi.org/10.3390/electronics14091694 - 22 Apr 2025

Abstract

Battery electric vehicles (BEVs) have emerged as a cornerstone of sustainable transportation systems, driving a fundamental transformation in public transport (PT) operations over the past decade. The unique characteristics of BEVs, including range limitations and battery degradation dynamics, necessitate a multi-dimensional optimization framework [...] Read more.

Battery electric vehicles (BEVs) have emerged as a cornerstone of sustainable transportation systems, driving a fundamental transformation in public transport (PT) operations over the past decade. The unique characteristics of BEVs, including range limitations and battery degradation dynamics, necessitate a multi-dimensional optimization framework that simultaneously considers energy supply management, operational efficiency, and battery lifecycle optimization in transit scheduling and timetabling. This paper presents a systematic review of BEV timetabling and scheduling research, structured around three main contributions. First, it comprehensively examines the evolution of electric vehicle timetabling problems, providing a detailed comparative analysis of methodological approaches in this domain. Second, it identifies and critically evaluates key developments in electric vehicle scheduling, including extended research directions (such as the integration with crew scheduling) and their practical implications. Third, it investigates the integration of BEV scheduling and timetabling, synthesizing the strengths and limitations of current methodologies while outlining promising avenues for future research. By offering a comprehensive analysis of the advancements in battery electric public transport scheduling over the past decade, this review serves as both a technical reference and a strategic guide for researchers and practitioners in the field of sustainable transportation systems. Full article

(This article belongs to the Special Issue Sustainable Transportation Systems)

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8392 KiB

Open AccessArticle

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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6047 KiB

Open AccessArticle

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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10136 KiB

Open AccessArticle

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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1326 KiB

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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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4323 KiB

Open AccessArticle

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

Open AccessArticle

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, 1143 KiB

Open AccessReview

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)

17 pages, 18396 KiB

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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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