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Electronics, Volume 15, Issue 5 (March-1 2026) – 232 articles

Cover Story (view full-size image): This paper presents a compact dual-port circularly polarized MIMO dielectric resonator antenna operating in the 28 GHz band. A single cross-shaped dielectric resonator excited by orthogonal microstrip feeds generates near-degenerate hybrid modes, enabling circular polarization at both ports without perturbation cuts or decoupling structures. The antenna fully covers the FCC 28 GHz allocation, achieving over 15 dB isolation, up to 7.6 dBi gain, and radiation efficiency above 93%. The design offers a compact and integration-friendly solution for 5G and emerging 6G mmWave terminals. View this paper
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17 pages, 2573 KB  
Article
Temperature Dependence Modeling and Design Optimization of VCEsat in Carrier-Storage Trench-Gate IGBTs
by Anning Chen, Yameng Sun, Kun Ma, Xun Liu, Yang Zhou and Sheng Liu
Electronics 2026, 15(5), 1138; https://doi.org/10.3390/electronics15051138 - 9 Mar 2026
Viewed by 267
Abstract
Insulated-gate bipolar transistor (IGBT) power modules suffer efficiency degradation at elevated operating junction temperatures. The thermal sensitivity of the collector–emitter saturation voltage (VCEsat) induces thermal stress imbalance, constraining system efficiency and reliability. A multi-resistor cascade network model for carrier-storage trench-gate [...] Read more.
Insulated-gate bipolar transistor (IGBT) power modules suffer efficiency degradation at elevated operating junction temperatures. The thermal sensitivity of the collector–emitter saturation voltage (VCEsat) induces thermal stress imbalance, constraining system efficiency and reliability. A multi-resistor cascade network model for carrier-storage trench-gate IGBTs (CS-IGBTs) is established. The simulation results agree with the measurements within 10% error. The model decomposes the temperature coefficient contributions of individual structural regions. Analysis reveals that the drift region resistance dominates the VCEsat temperature coefficient. Based on this finding, a co-doping strategy is proposed through simultaneously increasing the doping concentration in the carrier-storage layer and P+ collector. This approach reduces the temperature sensitivity of carrier mobility in the drift region, thereby optimizing VCEsat’s temperature sensitivity. For the fabricated 1200 V/40 A CS-IGBT, the VCEsat temperature coefficient decreases from 2.38 mV/K to 1.76 mV/K over 300 K to 450 K, which represents a 25.4% reduction. The total switching loss at 450 K decreases from 9.32 mJ to 8.70 mJ, achieving a 6.7% improvement. This device-level optimization suppresses VCEsat’s temperature sensitivity and switching losses, enhancing efficiency in high-temperature power module applications. Full article
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20 pages, 20209 KB  
Article
Planar-Guided Gaussian Splatting with Texture-Complexity-Based Initialization
by Anhong Zheng and Zhuoyuan Yu
Electronics 2026, 15(5), 1137; https://doi.org/10.3390/electronics15051137 - 9 Mar 2026
Viewed by 454
Abstract
Indoor scene reconstruction remains challenging due to the prevalence of low-texture regions such as walls, floors, and ceilings, where weak photometric signals hinder accurate geometric recovery. While 3D Gaussian Splatting (3DGS) achieves impressive novel view synthesis, existing methods struggle with geometric accuracy in [...] Read more.
Indoor scene reconstruction remains challenging due to the prevalence of low-texture regions such as walls, floors, and ceilings, where weak photometric signals hinder accurate geometric recovery. While 3D Gaussian Splatting (3DGS) achieves impressive novel view synthesis, existing methods struggle with geometric accuracy in textureless areas due to uniform treatment of scene regions. We propose a texture-complexity-based 3D Gaussian Splatting strategy that leverages geometric priors for high-fidelity indoor reconstruction. Our method extracts planar priors through Manhattan frame alignment and refines them with Segment Anything Model (SAM) masks, enabling texture-aware initialization: planar priors guide Gaussian placement in low-texture regions, while dense feature matching ensures accurate initialization in high-detail areas. During optimization, geometric regularization through depth-plane loss, normal-surface loss, and normal-consistency loss maintains structural integrity. Evaluations on ScanNet++, MuSHRoom, and Replica datasets demonstrate state-of-the-art performance, with training completed in under 1 h. Our approach balances geometric accuracy with photometric fidelity, providing a practical solution for high-fidelity indoor mesh extraction from Gaussian representations. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications, 2nd Edition)
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23 pages, 1856 KB  
Article
Efficient Anchor-Guided Multi-View Clustering via Diversity–Consistency Learning and Low-Rank Tensor Recovery
by Rong Fan, Kehan Kang, Qian Zhang, Chundan Liu, Yunhong Hu and Chong Peng
Electronics 2026, 15(5), 1136; https://doi.org/10.3390/electronics15051136 - 9 Mar 2026
Viewed by 261
Abstract
Multi-view clustering (MVC) is a fundamental unsupervised learning task for exploring latent structures from heterogeneous multi-view data. Existing MVC methods face critical challenges including the high computational cost of full-graph tensor models, neglect of high-order interactions between diversity and consistency information, and anchor [...] Read more.
Multi-view clustering (MVC) is a fundamental unsupervised learning task for exploring latent structures from heterogeneous multi-view data. Existing MVC methods face critical challenges including the high computational cost of full-graph tensor models, neglect of high-order interactions between diversity and consistency information, and anchor misalignment across different views. In this paper, we propose an efficient anchor-guided MVC framework (EAG-DCT) via diversity–consistency learning and low-rank tensor recovery. The proposed method jointly learns consensus anchors, view-specific diversity graphs, and a global consistency graph in a unified model that integrates all graphs into a high-order tensor to capture rich cross-view correlations. By imposing a nonconvex low-rank constraint on the tensor, we effectively enhance the synergy between diversity and consistency learning. Our framework achieves high computational efficiency and scalability for large-scale data. Comprehensive experimental results on benchmark datasets validate that EAG-DCT outperforms state-of-the-art MVC methods in both clustering effectiveness and efficiency. Full article
(This article belongs to the Collection Graph Machine Learning)
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39 pages, 1767 KB  
Systematic Review
Advanced Hardware Security on Embedded Processors: A 2026 Systematic Review
by Ali Kia, Aaron W. Storey and Masudul Imtiaz
Electronics 2026, 15(5), 1135; https://doi.org/10.3390/electronics15051135 - 9 Mar 2026
Viewed by 1116
Abstract
The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization [...] Read more.
The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization of NIST’s post-quantum cryptography standards and accelerated commercial deployment of hardware security primitives. Through analysis of the peer-reviewed literature, industry implementations, and standardization efforts, we survey five critical areas: post-quantum cryptography (PQC) implementations on resource-constrained hardware, physically unclonable functions (PUFs) for device authentication, hardware Roots of Trust and secure boot mechanisms, side-channel attack mitigations, and Trusted Execution Environments (TEEs) for microcontroller-class devices. For each domain, we analyze technical mechanisms, deployment constraints (power, memory, cost), security guarantees, and commercial maturity. Our review distinguishes itself through its integration perspective, examining how these primitives must be composed to secure real-world embedded systems, and its emphasis on post-standardization PQC developments. We highlight critical gaps including PQC memory overhead challenges, ML-resistant PUF designs, and TEE developer friction, while documenting commercial progress such as PSA Level 3 certified components and 500+ million PUF-enabled devices deployed. This synthesis provides practitioners with practical guidance for securing the next generation of IoT and embedded systems. Full article
(This article belongs to the Special Issue Design and Implementation of Embedded Systems for Real-Time Applications)
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19 pages, 2348 KB  
Article
IEC 61850-80-5-Based Data Mapping for Communication Modeling of Smart Inverters with IEC 61850 and Modbus Integration
by Taha Selim Ustun
Electronics 2026, 15(5), 1134; https://doi.org/10.3390/electronics15051134 - 9 Mar 2026
Viewed by 236
Abstract
In modern industrial systems, including power system automation, it is important to ensure that new standards are able to communicate with the older ones. IEC 61850 standard has been gaining significant ground in power system automation due to it is object-oriented design. In [...] Read more.
In modern industrial systems, including power system automation, it is important to ensure that new standards are able to communicate with the older ones. IEC 61850 standard has been gaining significant ground in power system automation due to it is object-oriented design. In line with its exponential growth, it is imperative to integrate IEC 61850 with other information exchange approaches. Modbus is a very robust communication protocol that uses registers. Since it can be deployed in a cost-effective way, it is widely used in older or lower-cost devices. Unlike IEC 61850, which supports real-time communication, Modbus envisions a trigger-based communication style. All of these fundamental differences make direct communication between these two protocols nontrivial. In order to address this need, IEC TR 61850-80-5 is developed to give a structured approach for mapping Modbus data into the IEC 61850 data model. This is conducted using a gateway and includes identifying relevant Modbus registers, converting the data format and embedding them into IEC 61850 logical nodes and data attributes. If completed, this allows legacy devices such as meters or sensors running on Modbus to be seamlessly integrated into modern smart-grid systems using IEC 61850. This paper shows how such integration can be performed between smart inverters and the sensors feeding information to them. Firstly, both protocols are introduced. Then, the IEC 618150 modeling of smart inverters is presented. Finally, data mapping is performed between Modbus registers of current- and voltage sensors and the said smart inverter model. A gateway is developed based on this mapping as well. By bridging two widely used protocols, this work supports interoperability, extends the life of existing assets and ensures a smooth transition towards digital power systems. Full article
(This article belongs to the Special Issue Recent Advances of Renewable Energy in Power Systems)
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20 pages, 2984 KB  
Article
Current Estimator LESO-Based Discrete-Time LADRC of a DC-DC Buck Converter
by Onur Demirel
Electronics 2026, 15(5), 1133; https://doi.org/10.3390/electronics15051133 - 9 Mar 2026
Viewed by 299
Abstract
This study proposes a systematic approach for implementing discrete-time Linear Active Disturbance Rejection Control in the closed-loop regulation of power converters. The continuous-time Linear Extended State Observer was discretized using the zero-order hold method to obtain a current estimator-based Linear Extended State Observer [...] Read more.
This study proposes a systematic approach for implementing discrete-time Linear Active Disturbance Rejection Control in the closed-loop regulation of power converters. The continuous-time Linear Extended State Observer was discretized using the zero-order hold method to obtain a current estimator-based Linear Extended State Observer that is suitable for real-time implementation. The design considerations for discrete-time Linear Active Disturbance Rejection Control, including the selection of observer and controller parameters and the sampling period, are addressed. For performance comparison, a PI controller was designed and implemented in discrete time. The control schemes were evaluated via MATLAB/Simulink (2025b) simulations and real-time closed-loop experiments on a microcontroller to assess the transient response, disturbance rejection capability, and steady-state accuracy of the buck converter. The simulation and experimental results demonstrate that the discrete-time Linear Active Disturbance Rejection Control incorporating a current-estimator-based Linear Extended State Observer significantly outperforms the PI controller in terms of transient response and disturbance rejection capability. From this perspective, this study provides a meaningful contribution to the limited literature on linear extended state observer-based discrete-time Active Disturbance Rejection Control methods. Full article
(This article belongs to the Special Issue Power Electronics and Multilevel Converters)
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20 pages, 701 KB  
Article
Global Anchor-Guided Local Anchor Learning for Multi-View Clustering
by Guangzheng Zhu, Chundan Liu, Qian Zhang, Kehan Kang, Yunhong Hu and Chong Peng
Electronics 2026, 15(5), 1132; https://doi.org/10.3390/electronics15051132 - 9 Mar 2026
Viewed by 266
Abstract
Multi-view clustering (MVC) is crucial for exploiting complementary information from multi-view data. Anchor-based MVC methods are efficient for large-scale tasks but lack the ability to balance view-specific local complementarity and cross-view global consistency. To address this issue, we propose GL4-MVC, a dual-level anchor [...] Read more.
Multi-view clustering (MVC) is crucial for exploiting complementary information from multi-view data. Anchor-based MVC methods are efficient for large-scale tasks but lack the ability to balance view-specific local complementarity and cross-view global consistency. To address this issue, we propose GL4-MVC, a dual-level anchor graph learning framework. It constructs anchor graphs with integrated adaptive learning of view-specific local anchors and concatenated a priori cross-view global anchor guidance, with an orthogonal mapping matrix enabling cross-level alignment to ensure effective guidance of global information for local learning. GL4-MVC is scalable and suitable for large-scale data. Extensive experimental results confirm the effectiveness and efficiency of GL4-MVC. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Image Classification)
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34 pages, 605 KB  
Article
A Physically Constrained KPP–Rate-and-State Reaction–Diffusion Framework for Stable Large-Scale Modeling of Stress Evolution
by Boi-Yee Liao
Electronics 2026, 15(5), 1131; https://doi.org/10.3390/electronics15051131 - 9 Mar 2026
Viewed by 320
Abstract
The emergence of large-scale models and machine learning has transformed the modeling of complex nonlinear systems, such as postseismic stress evolution. However, purely data-driven approaches often lack interpretability and numerical stability, leading to physically inconsistent long-term predictions. This study addresses these limitations by [...] Read more.
The emergence of large-scale models and machine learning has transformed the modeling of complex nonlinear systems, such as postseismic stress evolution. However, purely data-driven approaches often lack interpretability and numerical stability, leading to physically inconsistent long-term predictions. This study addresses these limitations by introducing a coupled Kolmogorov–Petrovsky–Piskunov–Rate-and-State (KPP–RS) reaction–diffusion system as a rigorous physical prior for large-scale modeling of stress-driven dynamics. Using analytic semigroup theory and Banach’s fixed-point theorem, we establish the global existence and uniqueness of solutions, ensuring that the governing dynamics are mathematically well posed—a necessary prerequisite for stable learning-based frameworks. We further prove the global dissipativity of the system and identify a bounded absorbing set in the H1 phase space, which imposes intrinsic physical constraints and limits unphysical parameter exploration in large-scale optimization or black-box modeling. In addition, a Courant–Friedrichs–Lewy (CFL) stability condition is derived, providing a theoretical benchmark for time-step selection in numerical implementations, including physics-informed or hybrid neural architectures. This analytical framework supplies a mathematically controlled foundation for developing robust, interpretable, and stable pattern-recognition or time-series representations in complex geophysical systems. Full article
(This article belongs to the Special Issue Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges)
23 pages, 8187 KB  
Article
A Secure UAV Swarm Architecture Based on Dynamic Heterogeneous Redundancy and Cooperative Supervision
by Wutao Qin, Qiang Li, Qi Liu and Zhenkai Wang
Electronics 2026, 15(5), 1130; https://doi.org/10.3390/electronics15051130 - 9 Mar 2026
Viewed by 296
Abstract
Current Unmanned Aerial Vehicle (UAV) swarm designs prioritize physical reliability over network security, leaving systems vulnerable to increasingly sophisticated cyber threats in complex environments. Existing defense methods are mostly limited to peripheral network security technologies, such as encryption, authentication, and firewalls. Consequently, they [...] Read more.
Current Unmanned Aerial Vehicle (UAV) swarm designs prioritize physical reliability over network security, leaving systems vulnerable to increasingly sophisticated cyber threats in complex environments. Existing defense methods are mostly limited to peripheral network security technologies, such as encryption, authentication, and firewalls. Consequently, they lack deep integration at the formation architecture level. This separation results in a disconnect between system reliability design and security protection mechanisms, making it difficult to effectively deal with high-level security threats such as internal backdoor vulnerabilities. To this end, this paper proposes an endogenous security architecture for UAV swarm based on dynamic heterogeneous redundancy (DHR) and cooperative supervision. Firstly, a theoretical model of DHR system for UAV swarm was constructed, and discrete nodes are abstracted as dynamic heterogeneous resource pools. Through the formal definition of the heterogeneous executor space, redundancy adjudication mechanism, and dynamic scheduling method, we demonstrate how this architecture suppresses common mode failures by introducing internal and external uncertainties, thereby realizing the coordination and unification of safety and security. Secondly, a distributed security control strategy based on cooperative supervision is proposed, which uses cross-validation between neighbors to replace the centralized adjudication of traditional DHR, solves the problem of anomaly detection in a decentralized environment, and combines reactive cleaning and periodic disturbance scheduling to give the system the ability to self-heal against unknown threats. Simulations in various attack scenarios demonstrate the proposed method’s superiority over traditional architectures. Especially in the simulated dormant multi-mode Advanced Persistent Threat (APT) scenario, the system can still maintain availability of more than 81%, which effectively verifies the key role of the coordination mechanism of heterogeneity, redundancy and dynamics in enhancing the safety and security of UAV swarms. Full article
(This article belongs to the Special Issue Hardware and Software Co-Design in Intelligent Systems)
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20 pages, 443 KB  
Article
Adaptive Energy—Accuracy Trade-Offs in Configurable MAC Architectures for AI Acceleration
by Turki Alnuayri and Ibrahim Haddadi
Electronics 2026, 15(5), 1129; https://doi.org/10.3390/electronics15051129 - 9 Mar 2026
Viewed by 367
Abstract
Energy efficiency has become a primary bottleneck in hardware platforms supporting machine learning workloads, particularly as modern inference and training tasks demand sustained high-throughput computation. This challenge is further amplified in energy-harvesting and intermittently powered systems, where the available energy budget varies over [...] Read more.
Energy efficiency has become a primary bottleneck in hardware platforms supporting machine learning workloads, particularly as modern inference and training tasks demand sustained high-throughput computation. This challenge is further amplified in energy-harvesting and intermittently powered systems, where the available energy budget varies over time. This work introduces a run-time configurable multiply–accumulate (MAC) architecture that dynamically adjusts arithmetic precision to match instantaneous energy availability. The proposed design relies on an internally adaptive multiplier based on bit-level logic compression, enabling controlled modulation of power consumption while preserving numerical robustness. Crucially, the MAC maintains a fixed external operand interface, allowing for seamless precision adaptation without operand reformulation or datapath disruption. The architecture is implemented in System Verilog and evaluated using both ASIC synthesis in a 90 nm CMOS technology and FPGA deployment. Experimental results demonstrate approximately a fourfold improvement in power–delay product (PDP) relative to full-precision operation, with only limited degradation in inference accuracy. Full article
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17 pages, 5768 KB  
Article
Utilizing the Intrinsic CC/CV Characteristics of a CLLC Converter for Battery Charging with ZVS Operation
by Tiancheng Cao, Junjie Zhu, Yumeng Guo, Yi Han, Bo Wu and Dayi Li
Electronics 2026, 15(5), 1128; https://doi.org/10.3390/electronics15051128 - 9 Mar 2026
Cited by 1 | Viewed by 263
Abstract
In conventional CLLC topologies, CC/CV charging is typically implemented using closed-loop control strategies based on phase shift modulation. This not only increases control complexity but also requires additional voltage and current sensing circuits, thereby raising the overall system cost. To address these issues, [...] Read more.
In conventional CLLC topologies, CC/CV charging is typically implemented using closed-loop control strategies based on phase shift modulation. This not only increases control complexity but also requires additional voltage and current sensing circuits, thereby raising the overall system cost. To address these issues, this paper proposes a novel CC/CV charging strategy. By analyzing the inherent characteristics of the coupled network, the switching frequencies corresponding to the CC and CV operating points are derived. By jointly applying frequency modulation and phase shift control to the CLLC converter, the system not only realizes CC/CV charging, but also enables the regulation of both the magnitude and the direction of power flow. Furthermore, to improve the system’s efficiency, a fine frequency tuning method is introduced to ensure operation under the critical zero-voltage switching (ZVS) condition. Finally, a 500 W prototype is constructed to validate the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Advances in Electric Vehicle Technology)
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23 pages, 11610 KB  
Article
Channel-Robust RF Fingerprinting via Adversarial and Triplet Losses
by M. Zahid Erdoğan and Selçuk Taşcıoğlu
Electronics 2026, 15(5), 1127; https://doi.org/10.3390/electronics15051127 - 9 Mar 2026
Viewed by 363
Abstract
Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training [...] Read more.
Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to 88.52% average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a 5% performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of 96.71% is achieved on experimentally collected test data from an unseen location. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 3rd Edition)
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26 pages, 894 KB  
Article
Differential and Linear Cryptanalysis of the IoT-Friendly MGFN Block Cipher
by Namil Kim, Wonwoo Song, Seungjun Baek, Yongjin Jeon, Giyoon Kim, Changhoon Lee and Jongsung Kim
Electronics 2026, 15(5), 1126; https://doi.org/10.3390/electronics15051126 - 9 Mar 2026
Viewed by 247
Abstract
Developed in 2023, the Modified Generalized Feistel Network (MGFN) is a block cipher that complies with Malaysia’s national cryptographic and cybersecurity policies. MGFN is a 64-bit block cipher with a 128-bit master key, specifically designed to deliver lightweight cybersecurity in resource-constrained Internet of [...] Read more.
Developed in 2023, the Modified Generalized Feistel Network (MGFN) is a block cipher that complies with Malaysia’s national cryptographic and cybersecurity policies. MGFN is a 64-bit block cipher with a 128-bit master key, specifically designed to deliver lightweight cybersecurity in resource-constrained Internet of Things (IoT) environments. In this paper, we analyze the security of the full-round MGFN against differential and linear cryptanalysis. We present concrete key recovery strategies for both attacks by employing multiple peeling-off steps. As a result, for the first time, we demonstrate a practical differential cryptanalysis of the full-round MGFN within a realistic time bound. In addition, we propose a practical linear cryptanalysis of the round-reduced MGFN. Our results provide the first practical security assessment of MGFN and offer concrete insights into its resistance against differential and linear cryptanalysis, thereby supporting the design and evaluation of lightweight block ciphers for IoT environments. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity, Resilience, and Trust Frameworks for Future Urban IoT Systems)
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23 pages, 356 KB  
Review
A Review of Formal Methods in Quantum-Circuit Verification
by Arun Govindankutty
Electronics 2026, 15(5), 1125; https://doi.org/10.3390/electronics15051125 - 9 Mar 2026
Viewed by 479
Abstract
Quantum computing exploits the principles of quantum mechanics to perform computation. Information is stored in qubits and processed with a sequence of quantum gates arranged as circuits. Verifying the correctness of quantum circuits is becoming essential as hardware scales in qubit count and [...] Read more.
Quantum computing exploits the principles of quantum mechanics to perform computation. Information is stored in qubits and processed with a sequence of quantum gates arranged as circuits. Verifying the correctness of quantum circuits is becoming essential as hardware scales in qubit count and architectural complexity. Traditional testing and naive simulation do not scale and quickly become computationally infeasible because the state space grows exponentially. This creates a strong need for more powerful and scalable verification techniques. Formal methods offer a viable solution by providing mathematically rigorous and scalable verification techniques that address these scalability challenges through abstraction, symbolic reasoning, and probabilistic guarantees. This study examines how formal methods are applied to quantum-circuit verification. Specifically, four families of formal techniques: barrier certificates, abstract interpretation, model checking, and theorem proving are examined, along with the theoretical foundations and practical applications of these techniques. Finally, the study highlights open challenges and identifies promising directions for future research. An extensive set of references is included to support further study and exploration. Full article
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23 pages, 4201 KB  
Article
A Game-Theoretic Intention Planning Method for Autonomous Vehicles
by Sishen Li, Hsin Guan and Xin Jia
Electronics 2026, 15(5), 1124; https://doi.org/10.3390/electronics15051124 - 9 Mar 2026
Viewed by 314
Abstract
Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions [...] Read more.
Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions between the ego vehicle (EV) and target vehicle (TV) in pairwise scenarios. First, the study defines an intention representation method that characterizes intentions using spatial area boundaries, feasible speed ranges, and a set of goal points (speed goal points, position-orientation goal points). Second, a spatial motion planning approach is adopted to evaluate the intention, which optimizes the driving scheme using a multi-objective cost function (incorporating pursuit precision, comfort, energy efficiency, and travel efficiency). Finally, the game-theoretic decision-making model is constructed. The Social Value Orientation (SVO) is introduced to quantify drivers’ social preferences, and the payoff function, which integrates safety rewards (based on inter-vehicle distance) and performance rewards (based on motion planning indices), is established. Simulation results verify that the proposed model can effectively address the interactive intention decision-making problem between the AV and other road users and handle different scenarios. Full article
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34 pages, 12341 KB  
Article
Automated Vegetable Classification Using Hybrid CNN and Vision Transformer Models for Food Quality Assessment
by Azeddine Mjahad and Alfredo Rosado-Muñoz
Electronics 2026, 15(5), 1123; https://doi.org/10.3390/electronics15051123 - 9 Mar 2026
Viewed by 324
Abstract
The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences, and subtle morphological similarities between categories. This study evaluates the effectiveness of [...] Read more.
The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences, and subtle morphological similarities between categories. This study evaluates the effectiveness of combining CNNs with four advanced Vision Transformer (ViT) architectures: DeiT (Data-efficient Image Transformer), CoaT (Co-Scale Conv-Attentional Transformer), CvT (Convolutional Vision Transformer), CrossViT (Cross-Attention Vision Transformer) for the automatic classification of 15 vegetable types. All models were implemented within a unified CNN–ViT hybrid framework to enhance both local feature extraction and global contextual reasoning. We processed all images under identical conditions to ensure a fair comparison and reproducibility. Results demonstrate that the hybrid architectures significantly outperform the standalone CNN baseline, with CvT achieving an approximate global accuracy in the range of 96.6–98.88% and consistently strong performance across visually complex classes such as cabbage, brinjal, and pumpkin. These findings confirm that hybrid CNN–ViT models are highly effective for visual food analysis, offering a robust and scalable solution for quality control, automated inspection, and classification of agricultural products. The methodology presented here may also be extended to other food items, including gels and processed products, highlighting its versatility and industrial relevance. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications, 2nd Edition)
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28 pages, 3011 KB  
Article
Memory Isolation and Privilege Restriction-Based Virtual Machine Protection Method
by Xinlong Wu, Xun Gong, Miaomiao Yang, Guosheng Huang, Yingzhi Shi and Ping Dong
Electronics 2026, 15(5), 1122; https://doi.org/10.3390/electronics15051122 - 9 Mar 2026
Viewed by 415
Abstract
Data in multi-tenant cloud environments is increasingly shared across organizations, making strong in-memory isolation a critical requirement. Existing confidential computing mechanisms such as AMD SEV provide hardware-enforced protection, but they require specialized processors and incur non-trivial performance overhead, which limits their deployment in [...] Read more.
Data in multi-tenant cloud environments is increasingly shared across organizations, making strong in-memory isolation a critical requirement. Existing confidential computing mechanisms such as AMD SEV provide hardware-enforced protection, but they require specialized processors and incur non-trivial performance overhead, which limits their deployment in heterogeneous clouds. This paper presents DASPRI, a software-based approach that constructs an isolated execution environment for trusted virtual machines by combining dual address spaces with privilege restriction. DASPRI partitions physical memory into a normal region and an isolated region on NUMA systems, and steers all memory allocations of trusted VMs into the isolated region by monitoring page faults and kernel allocation paths. It further hardens the isolated region by mediating direct and dynamic kernel mappings and by maintaining separate page caches for trusted and normal VMs. Remote attestation is integrated to protect the integrity of metadata used to identify trusted VMs. We implement DASPRI on a HUAWEI Kunpeng AArch64 server running OpenEuler and evaluate it using microbenchmarks and UnixBench. Experimental results show that DASPRI enforces strong memory isolation with less than 5% overhead on basic system operations and only 1.3% degradation in overall host performance. Full article
(This article belongs to the Section Computer Science & Engineering)
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21 pages, 1560 KB  
Article
QEMU-Based 1553B Bus Simulation and Precise Timing Modeling Method
by Haitian Gao, Weijun Lu, Yiwen Fu, Wentao Ye and Xiaofei Guo
Electronics 2026, 15(5), 1121; https://doi.org/10.3390/electronics15051121 - 9 Mar 2026
Viewed by 311
Abstract
Deterministic, microsecond-level timing reproduction in full-system virtualization remains a key challenge for hardware-in-the-loop simulation of timing-sensitive communication buses. This paper presents a virtual time-driven approach that models protocol timing semantics as discrete events on a deterministic virtual timeline, and validates it using MIL-STD-1553B, [...] Read more.
Deterministic, microsecond-level timing reproduction in full-system virtualization remains a key challenge for hardware-in-the-loop simulation of timing-sensitive communication buses. This paper presents a virtual time-driven approach that models protocol timing semantics as discrete events on a deterministic virtual timeline, and validates it using MIL-STD-1553B, a representative aerospace bus with strict microsecond-level requirements, as a case study. The MIL-STD-1553B data bus is widely used in aerospace and high-reliability embedded systems, where communication correctness depends not only on message formats but also critically on microsecond-level timing semantics such as message intervals, frame periods, response timeouts, and automatic retries. However, existing Quick Emulator (QEMU)-based virtualization solutions typically rely on host scheduling for timing, making it difficult to maintain determinism under varying loads, which may lead to missed detections or false alarms in timeout/retry behaviors. This paper implements a configurable BU-64843 device model supporting bus controller (BC), remote terminal (RT), and monitor terminal (MT) multi-role switching under a unified framework and completes behavioral modeling of both legacy and enhanced bus controllers covering message scheduling, execution, and exception handling paths. We propose a virtual time-driven precise timing modeling method that explicitly models key timing semantics as discrete events on a virtual timeline. Extensive experiments across 10 timing scenarios demonstrate that our method reduces timing deviation from an average of 8 µs to 65–124 ns (99.1% improvement), achieving deterministic simulation decoupled from host execution speed while meeting the 1 µs minimum resolution requirement. While demonstrated on 1553B, the virtual time-driven method is applicable to other timing-sensitive bus protocols in QEMU-based simulation environments, offering a low-cost, reproducible, and high-precision simulation environment for protocol compliance verification, driver development, and system integration. Full article
(This article belongs to the Section Computer Science & Engineering)
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11 pages, 350 KB  
Article
Practical Timing Synchronization for OTFS for NTN Scenario
by Vladislav Borshch, Eugeniy Rogozhnikov and Artem Konovalchikov
Electronics 2026, 15(5), 1120; https://doi.org/10.3390/electronics15051120 - 9 Mar 2026
Viewed by 302
Abstract
Accurate time and frequency acquisition is essential for deploying Orthogonal Time–Frequency Space (OTFS) modulation in non-terrestrial networks (NTNs), where severe Doppler shifts and low-SNR conditions are common. We propose a practical synchronization method that inserts an m-sequence-based pilot (illustrated using the 5G NR [...] Read more.
Accurate time and frequency acquisition is essential for deploying Orthogonal Time–Frequency Space (OTFS) modulation in non-terrestrial networks (NTNs), where severe Doppler shifts and low-SNR conditions are common. We propose a practical synchronization method that inserts an m-sequence-based pilot (illustrated using the 5G NR PSS) periodically in the delay–Doppler grid. Leveraging OTFS mapping properties, the method enables robust matched-filter detection for joint coarse time and frequency acquisition and continuous phase-drift tracking without increasing transmission redundancy. Numerical simulations show that the proposed method achieves a slightly lower PAPR and approximately a 3 dB improvement in detection threshold compared to a recent practical baseline. The algorithm is suitable for 5G/6G NTN links such as LEO constellations and operates reliably at low and negative SNR values. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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20 pages, 1396 KB  
Article
A Cascaded Framework for Vehicle Detection in Low-Resolution Traffic Surveillance Videos
by Tao Yu and Laura Sevilla-Lara
Electronics 2026, 15(5), 1119; https://doi.org/10.3390/electronics15051119 - 8 Mar 2026
Viewed by 360
Abstract
Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and [...] Read more.
Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and false detections in traditional object detection algorithms trained on high-resolution data. To address this issue, this study proposes a cascaded collaborative framework that integrates video super-resolution (VSR) and object detection for robust perception in low-quality traffic surveillance scenarios. First, a transformer-based VSR model with masked intra- and inter-frame attention (MIA-VSR) is employed to reconstruct temporally coherent high-resolution video sequences from degraded inputs. A domain-specific super-resolved dataset is subsequently constructed to train a lightweight one-stage detector (You Only Look One-level Feature, YOLOF) for efficient vehicle localisation. Extensive experiments on public datasets (REDS, Vimeo90k, UA-DETRAC) demonstrate that the proposed framework achieved a 56.89 mAP@0.5 on low-resolution UA-DETRAC, outperforming both direct low-resolution inference (39.17 mAP@0.5) and conventional fine-tuning strategies (45.70 mAP@0.5) by 17.72 and 11.19 points, respectively. These findings indicate that super-resolution-driven data reconstruction provides an effective pathway for mitigating feature degradation in low-quality surveillance environments, offering both theoretical insight and practical value for intelligent transportation perception systems. Full article
(This article belongs to the Special Issue Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation)
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15 pages, 2052 KB  
Article
A Dual-Branch Multi-Scale Network for Skin Lesion Classification
by Ying Liu, Xinyu Feng, Yuchai Wan, Huifu Li, Xun Zhang and Abdureyim Raxidin
Electronics 2026, 15(5), 1118; https://doi.org/10.3390/electronics15051118 - 8 Mar 2026
Viewed by 274
Abstract
Dermoscopic images are widely used for diagnosing skin diseases, and automatic classification of lesion types using deep learning can significantly enhance diagnostic efficiency. However, challenges such as variations in imaging conditions, subtle differences between classes, high variability within classes, and severe class imbalance [...] Read more.
Dermoscopic images are widely used for diagnosing skin diseases, and automatic classification of lesion types using deep learning can significantly enhance diagnostic efficiency. However, challenges such as variations in imaging conditions, subtle differences between classes, high variability within classes, and severe class imbalance complicate skin lesion analysis. This paper introduces a dual-branch deep learning model where two branches independently process high-frequency and low-frequency image features to generate multi-scale fused representations. To address class imbalance, the model employs cosine similarity to strengthen inter-class discrimination and incorporates a bias term to improve recognition of minority lesion classes. Experiments conducted on the ISIC 2017 and ISIC 2018 datasets demonstrate that the proposed method surpasses state-of-the-art approaches, achieving accuracies of 97.0% and 91.9%, respectively, with sensitivity and specificity both exceeding 90% on the two datasets. Full article
(This article belongs to the Special Issue Deep Learning for Computer Vision Application: Second Edition)
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29 pages, 1565 KB  
Article
Integer Intelligence: A Reproducible Path from Training to FPGA
by Manjusha Shanker and Tee Hui Teo
Electronics 2026, 15(5), 1117; https://doi.org/10.3390/electronics15051117 - 8 Mar 2026
Viewed by 303
Abstract
A transparent, end-to-end pathway from learning-level training to deployable fixed-point hardware is presented and framed as gradients to gates. A didactic XOR convolutional network is first employed so that backpropagation, post-training quantization in INT8, and fixed-point arithmetic can be made concrete and verified [...] Read more.
A transparent, end-to-end pathway from learning-level training to deployable fixed-point hardware is presented and framed as gradients to gates. A didactic XOR convolutional network is first employed so that backpropagation, post-training quantization in INT8, and fixed-point arithmetic can be made concrete and verified with exact checks. The same methodology was applied to a compact LeNet-5 case study. On the software side, the training-to-export flow was formalized, and a bit-accurate Python reference was constructed for the quantized network. On the hardware side, a synthesizable INT8 datapath was implemented in Verilog, including multiply–accumulate units, sigmoid activation stages, and per-layer requantization with rounding and saturation. Test benches are provided so that the exported weights and activations can be ingested, and layer-wise matches can be reported. A co-simulation harness was used to coordinate framework inference, quantization, file conversion, HDL simulation, and regression checks, which enabled deterministic comparisons of the activations, partial sums and outputs. The complete loop was mapped to Artix-7 on the CMOD A7 development board, and the resource usage, maximum clock frequency, inference latency, and throughput were determined. The approach aligns with an educational HDL-to-Caffe pipeline by using reusable parameterized Verilog primitives for convolution, pooling, activation, and fully connected layers, training in Colab with AccDNN, Caffe, quantization, and an automated bit-for-bit verification regime before FPGA synthesis. Methodological contributions are provided, including a minimal and auditable XOR CNN that exposes scales, shifts, and saturation; a practical quantization recipe with INT32 accumulation and unit tests that guarantee agreement within one least significant bit between RTL and the INT8 reference; and a scalable mapping to LeNet-5 using a row-stationary and line-buffered dataflow on an Artix-7 FPGA. Empirical evidence shows feasibility at 100 MHz with representative utilization, millisecond-scale latency and zero mismatches across large test sets, which validates the quantization configuration and the verification strategy. Full article
(This article belongs to the Special Issue Recent Advances in AI Hardware Design)
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18 pages, 2797 KB  
Article
Variation-Aware Memristor-Based Analog Accelerator for Vision Transformer
by Qianhou Qu, Sheng Lu, Liuting Shang, Sungyong Jung, Qilian Liang and Chenyun Pan
Electronics 2026, 15(5), 1116; https://doi.org/10.3390/electronics15051116 - 8 Mar 2026
Viewed by 374
Abstract
Vision transformers (ViTs) have emerged as one of the most popular computer vision models, achieving remarkable performance in image recognition. However, ViTs require large-scale, high-dimensional matrix computations, and traditional digital accelerators, such as graphics processing units (GPUs), have memory bandwidth limitations, leading to [...] Read more.
Vision transformers (ViTs) have emerged as one of the most popular computer vision models, achieving remarkable performance in image recognition. However, ViTs require large-scale, high-dimensional matrix computations, and traditional digital accelerators, such as graphics processing units (GPUs), have memory bandwidth limitations, leading to higher latency, increased energy consumption, and larger area. To address this challenge, this paper proposes a memristor-based analog accelerator that leverages memristor crossbar arrays for in-memory computing, reducing data movement and improving computational efficiency. Considering the non-ideal characteristics of memristor devices and the influence of analog circuitry, we incorporate Gaussian-distributed analog computation error at each step and memristor non-ideality modeling into the ViT inference to enable realistic evaluation under hardware-level conditions. Experimental evaluation on ImageNet-1k dataset with TIMM-pretrained ViT models shows that the proposed analog accelerator can achieve the same Top-1 accuracy as a custom-designed 5 nm digital baseline accelerator, even with ~35% analog computation error and ~10% memristor conductance variation injected at each step. Compared to the digital counterpart, the proposed design achieves an 11.9× reduction in energy-delay product (EDP) and a 137.2× reduction in energy-delay-area product (EDAP). Full article
(This article belongs to the Section Circuit and Signal Processing)
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29 pages, 374 KB  
Review
The Dual Role of Grid-Forming Inverters: Power Electronics Innovations and Power System Stability
by Mahmood Alharbi
Electronics 2026, 15(5), 1115; https://doi.org/10.3390/electronics15051115 - 8 Mar 2026
Viewed by 617
Abstract
The transition from conventional synchronous generators to inverter-based power systems has introduced significant challenges in stability, reliability, and protection coordination. Grid-forming inverters (GFMs) have emerged as a promising solution by emulating inertia and voltage regulation functions while enabling grid-supportive operation in weak or [...] Read more.
The transition from conventional synchronous generators to inverter-based power systems has introduced significant challenges in stability, reliability, and protection coordination. Grid-forming inverters (GFMs) have emerged as a promising solution by emulating inertia and voltage regulation functions while enabling grid-supportive operation in weak or islanded networks. This study presents a structured qualitative review of the recent literature on GFM technologies. The selection process focused on control strategies, advanced semiconductor materials, protection frameworks, and cyber–physical security considerations. A thematic synthesis and comparative analysis were conducted to identify emerging trends and technical gaps. Among established approaches, virtual synchronous machine (VSM) and droop control remain widely adopted. More advanced strategies, including virtual oscillator control (VOC) and model predictive control (MPC), demonstrate improved dynamic performance in weak-grid conditions. Advances in semiconductor technologies, particularly Silicon Carbide (SiC) and Gallium Nitride (GaN), enable faster switching, higher efficiency, and enhanced thermal performance. The findings indicate a growing shift toward decentralized control architectures, fault-resilient converter topologies, and integrated protection–control co-design. Emerging solutions include grid-forming synchronization techniques that replace conventional phase-locked loop (PLL) structures, intrusion-tolerant inverter firmware with embedded anomaly detection, and predictive fault-clearing schemes tailored for low-inertia networks. Despite these advancements, several research gaps remain. These include limited large-scale validation of VOC and MPC strategies under high renewable penetration, insufficient interoperability metrics for legacy system integration, and a lack of standardized cybersecurity benchmarks across platforms. Future research should prioritize real-time experimental validation, robust protection co-design methodologies, and the development of regulatory and dynamic performance standards tailored to inverter-dominated grids. Strengthening protection coordination and interoperability frameworks will be essential to ensure the secure and stable deployment of GFMs in modern power systems. Full article
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19 pages, 6761 KB  
Article
Hybrid A*-Based Reverse Path-Planning of a Vehicle with Single Trailer
by Xincheng Cao, Haochong Chen, Bilin Aksun-Guvenc, Levent Guvenc, Brian Link, Peter J. Richmond, Dokyung Yim, Shihong Fan and John Harber
Electronics 2026, 15(5), 1114; https://doi.org/10.3390/electronics15051114 - 7 Mar 2026
Viewed by 275
Abstract
Reverse parking maneuvering of a vehicle with a trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the [...] Read more.
Reverse parking maneuvering of a vehicle with a trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path-planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle–trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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28 pages, 2136 KB  
Article
DP-JL: Differentially Private Steering via Johnson–Lindenstrauss Projection for Large Language Models
by Ziniu Liu, Yue Han, Yang Song, Zhuwei Zhang and Aiping Li
Electronics 2026, 15(5), 1113; https://doi.org/10.3390/electronics15051113 - 7 Mar 2026
Viewed by 358
Abstract
Steering large language models (LLMs) toward desired behaviors while preserving privacy is a critical challenge in AI alignment. Existing differentially private (DP) steering methods, such as PSA, add high-dimensional noise that can severely degrade steering accuracy. We propose DP-JL, a novel [...] Read more.
Steering large language models (LLMs) toward desired behaviors while preserving privacy is a critical challenge in AI alignment. Existing differentially private (DP) steering methods, such as PSA, add high-dimensional noise that can severely degrade steering accuracy. We propose DP-JL, a novel approach that combines Johnson–Lindenstrauss (JL) random projection with differential privacy to reduce noise while maintaining formal privacy guarantees. DP-JL projects steering vectors into a lower-dimensional space (dimension k) before adding DP noise, reducing total noise magnitude from O(d) to O(k) where k≪d, while the privacy budget ε remains unchanged. We evaluate DP-JL on seven behavioral datasets with LLaMA-2-7B, Mistral-7B, Qwen2.5-7B, and Gemma-2-9B, alongside general capability benchmarks (MMLU, TruthfulQA). All accuracy values are measured on held-out test sets. Results show that DP-JL achieves: (1) up to 22.76 percentage points higher steering accuracy than PSA on the myopic-reward dataset (at fixed privacy budget ε≈0.22, δ=10−5); (2) 91.7% win rate on sycophancy with an average accuracy improvement of 3.01 percentage points; (3) systematic advantages in high-privacy regimes (ε<0.2); and (4) superior capability preservation on related tasks (TruthfulQA), achieving 6.6 percentage points better accuracy than PSA. Furthermore, visualizations and layer-sensitivity analyses reveal that DP-JL faithfully preserves the geometric structure of activation spaces, explaining its robustness. Our findings demonstrate that DP-JL offers superior privacy–utility trade-offs while better preserving model capabilities. Full article
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17 pages, 4021 KB  
Article
Dangerous Goods Detection in X-Ray Security Inspection Images Based on Improved YOLOv8-seg
by Ting Wang, Pengfei Yuan and Aili Wang
Electronics 2026, 15(5), 1112; https://doi.org/10.3390/electronics15051112 - 7 Mar 2026
Viewed by 360
Abstract
In X-ray security inspection imagery, hazardous object detection is challenged by severe object overlap/occlusion, ambiguous boundaries of small objects, and complex texture representations caused by material diversity. Although YOLOv8-seg provides real-time instance segmentation capability, it still has clear limitations in this application scenario. [...] Read more.
In X-ray security inspection imagery, hazardous object detection is challenged by severe object overlap/occlusion, ambiguous boundaries of small objects, and complex texture representations caused by material diversity. Although YOLOv8-seg provides real-time instance segmentation capability, it still has clear limitations in this application scenario. Specifically, the original SPPF module has limited ability to model long-range spatial dependencies, making it difficult to accurately separate boundaries of densely overlapped objects, while the C2f module is insufficient for multi-scale feature parsing of hazardous items with diverse sizes and materials and introduces feature redundancy, which degrades segmentation accuracy in occluded scenes. To address these issues, this paper proposes an improved YOLOv8-seg framework for X-ray hazardous object detection, termed LM-YOLOv8. For feature enhancement, an SPPF-LSKA module is constructed by integrating large-kernel separable attention with dynamic receptive-field adjustment, thereby improving global contextual modeling and alleviating boundary ambiguity. For multi-scale feature fusion, a C2f-MSC module is designed by combining multi-branch dilated convolutions with the C2f structure to enhance complex contour parsing and cross-scale feature interaction. Experiments on the PIDray dataset show that the proposed method achieves 84.8% mAP50 in instance segmentation, representing an improvement of approximately 4.0 percentage points over the baseline YOLOv8-seg. In addition, the method demonstrates stronger robustness on challenging hard/hidden subsets, validating its effectiveness for X-ray security inspection hazardous object detection. Full article
(This article belongs to the Special Issue Image Processing, Target Tracking and Recognition System Design)
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23 pages, 15691 KB  
Article
ProM-Pose: Language-Guided Zero-Shot 9-DoF Object Pose Estimation from RGB-D with Generative 3D Priors
by Yuchen Li, Kai Qin, Haitao Wu and Xiangjun Qu
Electronics 2026, 15(5), 1111; https://doi.org/10.3390/electronics15051111 - 7 Mar 2026
Viewed by 418
Abstract
Object pose estimation is fundamental for robotic manipulation, autonomous driving, and augmented reality, yet recovering the full 9-DoF state (rotation, translation, and anisotropic 3D scale) from RGB-D observations remains challenging for previously unseen objects. Existing methods either rely on instance-specific CAD models, predefined [...] Read more.
Object pose estimation is fundamental for robotic manipulation, autonomous driving, and augmented reality, yet recovering the full 9-DoF state (rotation, translation, and anisotropic 3D scale) from RGB-D observations remains challenging for previously unseen objects. Existing methods either rely on instance-specific CAD models, predefined category boundaries, or suffer from scale ambiguity under sparse observations. We propose ProM-Pose, a unified cross-modal temporal perception framework for zero-shot 9-DoF object pose estimation. By integrating language-conditioned generative 3D shape priors as canonical geometric references, an asymmetric cross-modal attention mechanism for spatially aware fusion, and a decoupled pose decoding strategy with temporal refinement, ProM-Pose constructs metrically consistent and semantically grounded representations without relying on category-specific pose priors or instance-level CAD supervision. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate that ProM-Pose achieves competitive or superior performance compared to category-level methods, with mAP of 75.0% at 5°,2cm and 90.5% at 10°,5cm on CAMERA25, and 42.2% at 5°,2cm and 76.0% at 10°,5cm on REAL275 under zero-shot cross-domain evaluation. Qualitative results on real-world logistics scenarios further validate temporal stability and robustness under occlusion and lighting variations. ProM-Pose effectively bridges semantic grounding and metric geometric reasoning within a unified formulation, enabling stable and scale-aware 9-DoF pose estimation for previously unseen objects under open-vocabulary conditions. Full article
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23 pages, 19657 KB  
Article
Analysis of Power Characteristics in Micro-Source Half-Bridge Converter Series Microgrids Based on VCLPD-SPWM
by Sheng Xue and Zhenyang Zhang
Electronics 2026, 15(5), 1110; https://doi.org/10.3390/electronics15051110 - 7 Mar 2026
Viewed by 239
Abstract
In half-bridge converter series microgrid (HBCS-MG) systems, output fluctuations caused by varying wind speeds and solar shading induce active power imbalances among generation modules (GMs). This imbalance increases susceptibility to overmodulation distortion and restricts the active power regulation range. To address these challenges, [...] Read more.
In half-bridge converter series microgrid (HBCS-MG) systems, output fluctuations caused by varying wind speeds and solar shading induce active power imbalances among generation modules (GMs). This imbalance increases susceptibility to overmodulation distortion and restricts the active power regulation range. To address these challenges, this paper proposes a variable carrier level phase disposition SPWM (VCLPD-SPWM) strategy to enhance the active power regulation depth of GMs at the modulation level. Assuming a stable DC-link voltage for the half-bridge converters (HCs), the power distribution characteristics and switching durations of GMs under PD-SPWM are analytically examined. Subsequently, the carrier level transition points and periods for the maximum regulation range under VCLPD-SPWM are derived, alongside the corresponding power increments and negative power characteristics of each GM. Finally, theoretical calculations, simulations, and experimental results validate the feasibility and effectiveness of the proposed strategy, demonstrating its superiority over carrier phase-shifted modulation strategies. Full article
(This article belongs to the Section Power Electronics)
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24 pages, 5269 KB  
Article
Non-Cooperative Power Allocation Game in Distributed Radar Systems: A Sigmoid Utility-Based Approach
by Yuan Huang, Ke Li, Weijian Liu and Tao Liu
Electronics 2026, 15(5), 1109; https://doi.org/10.3390/electronics15051109 - 7 Mar 2026
Viewed by 284
Abstract
Power control algorithms using the signal-to-interference-plus-noise ratio (SINR) metric in distributed radar systems (DRS) may suffer from performance degradation in infeasible conditions. In this paper, we present a Sigmoid-based Power Allocation Game (SigPAG) algorithm for target detection in DRS to minimize total power [...] Read more.
Power control algorithms using the signal-to-interference-plus-noise ratio (SINR) metric in distributed radar systems (DRS) may suffer from performance degradation in infeasible conditions. In this paper, we present a Sigmoid-based Power Allocation Game (SigPAG) algorithm for target detection in DRS to minimize total power consumption while meeting predetermined target detection performance. Firstly, a physically interpretable Sigmoid function is designed to model radar detection probability as the utility function, overcoming the performance limitations and potential deviations of SINR-based utilities. Secondly, by integrating the proposed Sigmoid utility, SigPAG is established to describe the interaction among radar nodes in the DRS. The existence and uniqueness of the Nash equilibrium (NE) solution are proven by the closed-form expressions of the best response function. Furthermore, an iterative power allocation algorithm is proposed to adjust the transmit powers towards the NE point. Finally, simulation results obtained in a 4-node DRS with Radar Cross Section (RCS) values of [1, 0.3, 2, 5] m2 demonstrate that the proposed algorithm achieves an energy efficiency improvement of 36.1% in target detection compared with the traditional SINR-based methods. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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