Electronics

34 pages, 1580 KB

Open AccessFeature PaperArticle

Filter Before Mixing: Per-Modality Denoising for Multimodal RL with Application to Health Management

by Tsuyoshi Okita

Electronics 2026, 15(11), 2361; https://doi.org/10.3390/electronics15112361 - 29 May 2026

Multimodal reinforcement learning agents must fuse signals with vastly different noise profiles—yet existing architectures, whether monolithic (

π

0, DreamerV3) or modular (MSDP, VTDexManip), allow noise from unreliable modalities to contaminate reliable ones at the point of fusion. We propose filter before mixing: [...] Read more.

Multimodal reinforcement learning agents must fuse signals with vastly different noise profiles—yet existing architectures, whether monolithic (

π

0, DreamerV3) or modular (MSDP, VTDexManip), allow noise from unreliable modalities to contaminate reliable ones at the point of fusion. We propose filter before mixing: each modality’s representation is independently refined by a per-modality Flow Matching module before spectral-domain fusion via a Fourier Neural Operator (FNO) with a residual gate ensuring that refinement is never harmful. The resulting architecture, FreamerV1 (Filter-before-mixing dreamer), has 93M parameters (0.4M trainable). On MiniGrid, FreamerV1 reaches 87.7 ± 8.2% (3 seeds) at 5000 episodes, while the encoder-only baseline degrades to 78% due to catastrophic forgetting. With OGM-GE (On-the-fly Gradient Modulation) for adaptive per-modality gate control, FreamerV1 achieves an 8.0% relative improvement in success rate over manual tuning with halved seed-to-seed variance (three seeds). On Crafter (no language modality), it achieves an 11.7% relative improvement over DreamerV3 in the official Crafter score (geometric mean of 22 achievement success rates; 10 seeds). On PAMAP2 wearable sensors—where no pretrained encoder exists—the foundation encoder achieves 2.4× higher reward and 16× lower variance than a vanilla MLP, confirming that the filter-before-mixing advantage grows with encoder noise. Full article

(This article belongs to the Section Artificial Intelligence)

49 pages, 24347 KB

Open AccessArticle

Curvature-Adaptive Smoothing for Multi-View Industrial Metrology

by Jae Kyung Lee, Soon Woo Kwon, Hae Gwang Park, Seung Ki Baek and Min Young Kim

Electronics 2026, 15(11), 2360; https://doi.org/10.3390/electronics15112360 - 29 May 2026

Abstract

Three-dimensional point cloud data acquired in industrial environments inherently exhibit quality limitations, including measurement noise, local geometric irregularities, and surface roughness. These issues are commonly observed in both structured-light scanning and SIFT-based photogrammetric reconstruction, highlighting the necessity of post-processing in metrology applications where [...] Read more.

Three-dimensional point cloud data acquired in industrial environments inherently exhibit quality limitations, including measurement noise, local geometric irregularities, and surface roughness. These issues are commonly observed in both structured-light scanning and SIFT-based photogrammetric reconstruction, highlighting the necessity of post-processing in metrology applications where dimensional accuracy and geometric reliability are critical. However, conventional global-parameter smoothing methods, such as Savitzky–Golay filtering, LOWESS, and bilateral filtering, apply uniform smoothing intensity across regions with varying curvature, resulting in an inherent trade-off between noise suppression and geometry preservation. In this study, we propose a learning-independent post-processing framework that adaptively modulates smoothing strength by integrating local curvature estimation with unsupervised anomaly modeling. The proposed approach combines normal-variance-based curvature approximation with k-nearest neighbor anomaly scoring, while CAD data are employed exclusively as an external reference for evaluation. Experimental results on industrial product datasets demonstrate that, for structured-light reconstructions, the proposed method reduces average error to a level comparable to local regression-based smoothing while simultaneously achieving the highest edge-preservation index among all evaluated methods, thereby attaining an optimal operating point between noise suppression and geometric integrity. Although the suppression of extreme deviations remains limited, the geometry-preservation metrics are improved without a significant increase in average error. In contrast, SIFT+COLMAP-based reconstructions exhibit performance comparable to that of the original data, a behavior attributable to low-frequency systematic reconstruction biases rather than high-frequency sensor noise, which fall outside the corrective capacity of purely geometry-driven local smoothing. Full article

(This article belongs to the Special Issue Artificial Intelligence, Computer Vision and 3D Display, 2nd Edition)

► Show Figures

Figure 1

28 pages, 8452 KB

Open AccessArticle

Causal Graph-Enhanced Large Language Models for Automated Fault Diagnosis and Intelligent Operation and Maintenance in Distributed Computing Systems

by Yu Gu, Jian Zhang and Yugen Du

Electronics 2026, 15(11), 2359; https://doi.org/10.3390/electronics15112359 - 29 May 2026

Abstract

Modern distributed computing systems face increasingly complex architectural evolution and potentially costly failures, calling for efficient and robust automated diagnosis to ensure the stability of large-scale data processing. Existing data-driven approaches are constrained by scarce labeled data and black-box behaviors, while expert-based knowledge-driven [...] Read more.

Modern distributed computing systems face increasingly complex architectural evolution and potentially costly failures, calling for efficient and robust automated diagnosis to ensure the stability of large-scale data processing. Existing data-driven approaches are constrained by scarce labeled data and black-box behaviors, while expert-based knowledge-driven solutions suffer from high construction costs and insufficient coverage of dynamic scenarios, especially when domain expertise is limited. This work proposes a fault diagnosis framework that integrates a unified causal graph (UCG) with large language models (LLMs), leveraging a dual knowledge-driven and data-driven mechanism to construct causal graph representations and dynamically generate structured diagnostic reasoning chains-of-thought based on system state awareness. Here, “causal” is used in a restricted sense, combining knowledge-driven dependencies with data-driven statistical regularities. Experimental results indicate that, using GPT-4o as an example, this study achieves accurate fault identification across the eight evaluated fault scenarios within the controlled evaluation scope of this study. Labeled instances are partitioned using stratified sampling into 80% for training and 20% for held-out evaluation; the procedure is repeated five times with independent train–test partitions, and reported matching rates are averaged across these runs. Compared with baselines that rely solely on fault information or on symptom information, the fault matching rate improves by 41.4% and 33.5%, respectively. By tightly coupling structured causal logic with generative artificial intelligence, the approach significantly enhances the interpretability and reliability of the diagnostic process and provides high-value, expert-level support for intelligent operations and maintenance (O&M) in distributed computing systems. Full article

(This article belongs to the Special Issue Exploring Edge AI: Architectures, Algorithms, and the Role of Edge–Cloud Cooperation for Scalable AI Systems)

► Show Figures

Figure 1

31 pages, 349 KB

Open AccessFeature PaperArticle

Crisis Disinformation and Verification Dynamics in the València 2024 DANA

by Juan José Climent-Ferrer, J. Ernesto Solanes, Ana Martí-Testón, Flavio Moriniello, Adolfo Muñoz and Luis Gracia

Electronics 2026, 15(11), 2358; https://doi.org/10.3390/electronics15112358 - 29 May 2026

Abstract

This study examines the circulation of disinformation during the 2024 València DANA (high-altitude isolated depression), which produced torrential rainfall across eastern and southern Spain between 29 and 31 October 2024. Using a quantitative content analysis, it analyzes the 100 most viral false or [...] Read more.

This study examines the circulation of disinformation during the 2024 València DANA (high-altitude isolated depression), which produced torrential rainfall across eastern and southern Spain between 29 and 31 October 2024. Using a quantitative content analysis, it analyzes the 100 most viral false or misleading claims, classifying them by typology, format, dissemination channel, and narrative strategy. Findings show an ecosystem dominated by conspiracy narratives about the causes of the disaster and by audiovisual content—particularly short videos and images—which achieved substantially greater reach than textual posts. Narrative mechanisms such as decontextualization, emotional appeal, and political polarization were recurrent and often combined. Verification efforts that matched the original format were associated with higher relative correction reach, although their observable diffusion remained lower than that of the false claims in the analyzed sample. Overall, the study highlights the cross-platform and multimodal dynamics of crisis disinformation and underscores the need for proactive, technologically supported communication strategies. These include automated monitoring, multimodal verification, and interoperable digital infrastructures for crisis communication. Full article

(This article belongs to the Special Issue Advances in Web Data Management)

► Show Figures

Figure 1

37 pages, 45876 KB

Open AccessArticle

Hierarchical Multi-Prototype Appearance Memory: A Plug-and-Play Module for Identity-Stable Online Multi-Object Tracking

by Wenning Zhang, Mintao Liu, Yangjie Cao, Jihao Cai, Chao Wang, Huili Xia and Kunming Xu

Electronics 2026, 15(11), 2357; https://doi.org/10.3390/electronics15112357 - 29 May 2026

Abstract

Online multi-object tracking (MOT) aims to maintain consistent target identities across video frames, yet it remains vulnerable to identity switches under occlusion and appearance variation. Many existing trackers rely on single-prototype exponential moving average (EMA) memory, which is efficient but prone to contamination, [...] Read more.

Online multi-object tracking (MOT) aims to maintain consistent target identities across video frames, yet it remains vulnerable to identity switches under occlusion and appearance variation. Many existing trackers rely on single-prototype exponential moving average (EMA) memory, which is efficient but prone to contamination, over-smoothing, and staleness. To address this issue, we propose Hierarchical Multi-Prototype Appearance Memory (HMP), a plug-and-play module for online MOT. HMP separates stable long-term identity anchors from short-term transitional evidence through a multi-prototype long-term memory and a short first-in-first-out (FIFO) queue. A unified joint reliability score governs memory writing and maintenance, and a frozen two-stage association strategy first performs stable primary matching and then allows conservative short-term recovery only on residual cases. Experiments on MOT17 and MOT20 show that HMP improves identity continuity while preserving competitive overall tracking quality. Controlled ablations further support the effectiveness of the proposed memory representation, reliability control, and staged evidence usage under fixed upstream modules. Full article

(This article belongs to the Special Issue Advances in Image Processing and Computer Vision)

► Show Figures

Figure 1

29 pages, 2844 KB

Open AccessArticle

Switched Bang–Bang Funnel Control for Fault Ride-Through Enhancement of Doubly-Fed Variable-Speed Pumped Storage Units

by Rufei He, Yumin Peng, Lei Xie, Fanqi Huang, Chao Wen, Wenbin Yan, Hanyuan Li and Yang Liu

Electronics 2026, 15(11), 2356; https://doi.org/10.3390/electronics15112356 - 29 May 2026

Abstract

This study addresses fault ride-through of doubly-fed pumped storage units by proposing switched bang–bang funnel controllers for machine- and grid-side converters. The objective is to enhance transient stability, current regulation, and DC-link voltage support during severe AC grid faults. The method combines funnel-based [...] Read more.

This study addresses fault ride-through of doubly-fed pumped storage units by proposing switched bang–bang funnel controllers for machine- and grid-side converters. The objective is to enhance transient stability, current regulation, and DC-link voltage support during severe AC grid faults. The method combines funnel-based error constraints with a switching logic that activates a bang–bang action only when tracking errors approach prescribed performance bounds, reverting to nominal regulation otherwise. High-fidelity electromagnetic transient simulations are conducted and benchmarked against a conventional PI-based controller under three-phase-to-ground fault scenarios. The results show that the switched controller achieves faster active/reactive power recovery with reduced overshoot, markedly suppresses current oscillations on both converters, and limits DC-link voltage dips while shortening the voltage restoration time. The switched controller also prevents the pumped storage unit operating in pumping mode from becoming unstable in the case of a metallic fault scenario. These findings indicate that the proposed strategy improves dynamic performance and fault ride-through capability without compromising steady-state behavior, providing a practical pathway toward compliance with grid-code requirements for pumped storage units under severe disturbances. Full article

(This article belongs to the Special Issue Innovations in Intelligent Microgrid Operation and Control, 2nd Edition)

► Show Figures

Figure 1

27 pages, 8456 KB

Open AccessArticle

AD-CapsFPN: An Asymmetric Dilated Convolutional Capsule Network with Feature Pyramid for Malware Classification

by Longcheng Wang, Jin Li, Yafei Song, Yanbing Ren and Yunfei Xu

Electronics 2026, 15(11), 2355; https://doi.org/10.3390/electronics15112355 - 29 May 2026

Abstract

Existing CNN-based visual malware classification methods are often constrained by inductive bias mismatch: standard isotropic convolution kernels and global pooling operations neglect the inherent structural anisotropy of malware images, and these methods struggle to address the spatial rearrangement of code blocks caused by [...] Read more.

Existing CNN-based visual malware classification methods are often constrained by inductive bias mismatch: standard isotropic convolution kernels and global pooling operations neglect the inherent structural anisotropy of malware images, and these methods struggle to address the spatial rearrangement of code blocks caused by obfuscation, which we term the “Malware Picasso Problem”. To overcome these limitations, we propose AD-CapsFPN, an end-to-end framework representing a significant step toward spatial reasoning over texture memorization, with a synergistic “Rectification–Fusion–Inference” mechanism. Our approach rectifies anisotropic inductive biases in the feature extraction stage, dynamically aggregates cross-scale discriminative features in intermediate layers, injects row-aware spatial biases, and adopts a global pooling-free spatial routing strategy in the classification stage, effectively reconstructing logical associations between obfuscated and scattered code blocks. Experiments on the large-scale Fusion dataset and the obfuscated Androdex dataset demonstrate significant performance improvements: our method achieves a 16.22% boost in macro F1-score over the MobileNetV4 baseline on the Fusion dataset (reaching 97.98%), and hits 92.45% macro F1-score on the highly challenging Androdex-Set1, outperforming state-of-the-art methods such as MDC-RepNet (88.97%) and TAEfficientNet (88.15%). This work confirms that embedding malware domain priors into architecture design is the key to robust malware classification. Full article

(This article belongs to the Special Issue AI in Cybersecurity, 3rd Edition)

► Show Figures

Figure 1

16 pages, 11489 KB

Open AccessArticle

Flexible Grid-Connected/Off-Grid Switching Control Strategy for Storage Inverter

by Jiran Zhu, Kehui Zhou, Haiguo Tang, Yi Zhang, Xiaochao Hou and Mei Su

Electronics 2026, 15(11), 2354; https://doi.org/10.3390/electronics15112354 - 29 May 2026

Abstract

Taking a dual-mode grid-connected/off-grid storage inverter as the research subject, control models for both grid-connected and off-grid operation modes were established. For the grid-to-off-grid transition, an improved adaptive active frequency drift islanding detection algorithm was proposed, which employs a cubic power-based detection method [...] Read more.

Taking a dual-mode grid-connected/off-grid storage inverter as the research subject, control models for both grid-connected and off-grid operation modes were established. For the grid-to-off-grid transition, an improved adaptive active frequency drift islanding detection algorithm was proposed, which employs a cubic power-based detection method when frequency deviation is small to reduce positive feedback speed, and a parabola-based detection method when frequency deviation is large to enhance positive feedback speed. Compared with the traditional active frequency drift islanding detection algorithm, the proposed method can ensure islanding detection speed while effectively reducing the current total harmonic distortion during grid-connected operation. Experiments conducted on a storage inverter prototype demonstrated stable operation in both grid-connected and off-grid modes. The results indicate that the proposed control strategy enables rapid identification of operating conditions and mode switching, significantly improving the stability and reliability of the inverter during transition, thus laying a foundation for the autonomous operation of dynamic microgrids. Full article

(This article belongs to the Special Issue Advanced Power Electronics and Energy Systems for Renewable Integration and Sustainable Power Systems)

► Show Figures

Figure 1

11 pages, 2092 KB

Open AccessArticle

50 kVA Three-Phase Variable-Speed Diesel Cogenerator: A Practical Case

by Juan José Calero, Juan Vicente Míguez and José Carpio

Electronics 2026, 15(11), 2353; https://doi.org/10.3390/electronics15112353 - 29 May 2026

Abstract

This paper presents a case study demonstrating the operation of a 50 kVA three-phase variable-speed diesel generator at a Spanish Antarctic research base, located in an area of special ecological and environmental value, under conditions of extreme humidity and temperature. It verifies the [...] Read more.

This paper presents a case study demonstrating the operation of a 50 kVA three-phase variable-speed diesel generator at a Spanish Antarctic research base, located in an area of special ecological and environmental value, under conditions of extreme humidity and temperature. It verifies the fuel savings achieved through the use of variable-speed technology compared to standard, constant-speed generators. Furthermore, given that the price of fuel is significantly higher due to the high cost and complexity of transporting it to the base, the fuel savings at the base represent a huge logistical advantage, quite apart, of course, from the environmental benefits of such savings. A key feature of the equipment presented is that it has a system for recovering waste heat from the combustion engine, which, when integrated into the base’s hot water system, is used to increase the domestic hot water capacity, adding value to the machine whilst also delivering fuel savings. Full article

(This article belongs to the Special Issue Power Electronics, Renewable Energy and Advanced Energy Management Systems)

► Show Figures

Figure 1

32 pages, 6817 KB

Open AccessArticle

Miniaturized CRPA Design for GPS Receivers with 0.3 λ Spacing and Hybrid Coupling Reduction

by Ömer C. Dabak, Sultan Can and Murat Üçüncü

Electronics 2026, 15(11), 2352; https://doi.org/10.3390/electronics15112352 - 28 May 2026

Abstract

This study explores the miniaturization of the Controlled Reception Pattern Antenna (CRPA) for Global Positioning System (GPS) receivers, addressing the challenge of mutual coupling, which adversely affects antenna performance. In this work, a miniaturized CRPA is designed and manufactured by using Rogers RO3006 [...] Read more.

This study explores the miniaturization of the Controlled Reception Pattern Antenna (CRPA) for Global Positioning System (GPS) receivers, addressing the challenge of mutual coupling, which adversely affects antenna performance. In this work, a miniaturized CRPA is designed and manufactured by using Rogers RO3006 substrate. To provide a performance benchmark, a four-element reference CRPA array was also designed with a 0.5 λ inter-element spacing, yielding an overall aperture size of 149.58 mm × 150.24 mm and a worst-case inter-element isolation larger than 14.4 dB. For the miniaturized CRPA, the target inter-element spacing was set to be 0.3 λ. To overcome isolation limitations, several coupling-mitigation techniques were developed and integrated into the miniaturized design. The final configuration consisted of a four-element CRPA, with each element rotated by 90° relative to its neighbor, inter-element slots incorporated into the shared ground-plane, and an individual ground plane segmentation to reduce surface–wave coupling. The proposed miniaturized CRPA achieved an overall footprint of 104.21 mm × 104.55 mm with the worst-case isolation exceeding 18.36 dB, surpassing the isolation performance of the reference array. This work demonstrates that it is possible to realize a compact CRPA with enhanced inter-element isolation by integrating tailored coupling suppression methods. Full article

(This article belongs to the Section Microwave and Wireless Communications)

► Show Figures

Figure 1

32 pages, 3081 KB

Open AccessArticle

Connectivity Assessment: Strength, Trend, and Regularity in Opportunistic Networks

by William C. da Rosa, Celso B. Carvalho, Marcel W. R. da Silva, Raphael M. Guedes, André C. Mendes and Waldir S. S. Junior

Electronics 2026, 15(11), 2351; https://doi.org/10.3390/electronics15112351 - 28 May 2026

Abstract

Routing in Opportunistic Networks (OppNets) is continuously challenged by intermittent connectivity and severe resource constraints. To address these limitations, this paper proposes CASTRO, a novel routing architecture, alongside its reinforcement learning extension, QL-CASTRO. The primary novelty lies in the mathematical modeling of disconnection [...] Read more.

Routing in Opportunistic Networks (OppNets) is continuously challenged by intermittent connectivity and severe resource constraints. To address these limitations, this paper proposes CASTRO, a novel routing architecture, alongside its reinforcement learning extension, QL-CASTRO. The primary novelty lies in the mathematical modeling of disconnection intervals (OFF-mode) to extract precise social indicators—Strength, Trend, and Regularity—providing a robust alternative to traditional encounter-frequency metrics. To overcome the latency penalties inherent to conservative social routing, QL-CASTRO integrates a tabular Q-Learning paradigm. This acts as a dynamic acceleration mechanism, fusing social metrics with autonomous delivery delay estimates and strict message retirement policies. Performance was rigorously evaluated using the ONE simulator across dense pedestrian (Helsinki) and sparse vehicular (Manaus) environments. The results demonstrate that both protocols achieve high delivery rates near 90%. Crucially, QL-CASTRO significantly reduces average delivery latency compared to the baseline CASTRO protocol while maintaining moderate overhead and low energy consumption. Ultimately, this hybrid approach offers a scalable, resource-efficient routing solution for dynamic IoT environments where system longevity and information integrity are paramount. Full article

► Show Figures

Figure 1

29 pages, 1910 KB

Open AccessArticle

Path Loss Prediction in Dense WSN–IoT Networks with Machine Learning Techniques Across Diverse Terrains for Energy-Efficient Connectivity

by George Papastergiou, Apostolos Xenakis, Dimitrios Kosmanos, Costas Chaikalis, Menelaos Panagiotis Papastergiou and Vasileios Priovolos

Electronics 2026, 15(11), 2350; https://doi.org/10.3390/electronics15112350 - 28 May 2026

Abstract

Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy [...] Read more.

Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy without explicitly linking statistical error metrics to system-level design parameters, thus limiting their practical interpretability in deployment scenarios. This work presents an extensive comparative evaluation among well-known propagation models versus machine learning regressors, and a lightweight convolutional neural network (CNN) for path loss prediction, using transmitter–receiver distance and carrier frequency as input features. A pairwise communication model is adopted to ensure consistent analysis across heterogeneous environments while preserving physical interpretability of the propagation process. Building upon this evaluation, a unified analytical framework is proposed that correlates path loss (PL) prediction accuracy to system-level metrics relevant to WSN–IoT design. Moreover, in this work we apply the Root Mean Square Error (RMSE) of the best-performing model as an empirical estimate of the shadowing standard deviation, under standard statistical assumptions, thereby allowing its direct use in link budget and fade margin calculations. Extensive experimental results across five heterogeneous wireless link datasets demonstrate that improved prediction accuracy leads to reduced transmission power requirements, lower energy consumption, enhanced communication reliability, and extended node lifetime. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)

► Show Figures

Figure 1

18 pages, 1975 KB

Open AccessArticle

A 140 GHz Two-Channel Transmitter in 40 nm Bulk CMOS

by Junkyu Lee, Changjung Lee, Jaegwan Kim and Munkyo Seo

Electronics 2026, 15(11), 2349; https://doi.org/10.3390/electronics15112349 - 28 May 2026

Abstract

This paper presents a 140 GHz two-channel transmitter in 40 nm bulk CMOS technology for D-band wireless communication systems. The transmitter employs a direct upconversion architecture with IQ Gilbert cell mixers and a shared ×9 frequency multiplier for local oscillator (LO) generation. [...] Read more.

This paper presents a 140 GHz two-channel transmitter in 40 nm bulk CMOS technology for D-band wireless communication systems. The transmitter employs a direct upconversion architecture with IQ Gilbert cell mixers and a shared ×9 frequency multiplier for local oscillator (LO) generation. The Lange coupler generates quadrature LO signals for I and Q paths, while the two-way four-stage differential power amplifier with cascade topology provides high output power. On-wafer measurement at 140 GHz LO frequency demonstrates a 9.9 dB conversion gain with a 5.5–6.1 GHz 3 dB bandwidth. The measured saturated output power is 10.1 dBm with an output 1 dB compression point of 6.5 dBm. The IQ imbalance remains within 2 dB across the 3 dB bandwidth. The fabricated transmitter occupies a chip area of 1.68 mm² and consumes 435 mW from a 1 V supply. The power density of 6.09 mW/mm² is the highest among reported CMOS-based D-band transmitters. The dual-channel architecture with shared LO generation enables MIMO transmission, spatial multiplexing, and diversity techniques while maintaining compact size and competitive power efficiency for high data rate wireless applications in the D-band frequency range. Full article

(This article belongs to the Section Microwave and Wireless Communications)

15 pages, 691 KB

Open AccessArticle

An Agent-Based Model of a Controlled Detonation System for Sandbox Analysis of Suspicious Software

by Yevheniia Ivanchenko, Mikolaj Karpinski, Mykola Ryzhakov, Ihor Ivanchenko, Patryk Mazurek and Pawel Sawicki

Electronics 2026, 15(11), 2348; https://doi.org/10.3390/electronics15112348 - 28 May 2026

Abstract

In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to [...] Read more.

In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to perform adaptive GUI interactions in a plausible, isolated execution environment. The controlled detonation process is formulated as a partially observable Markov decision process (POMDP), while the proposed proof-of-concept architecture combines initial profiling, VM preparation, multi-layer telemetry, and an RL policy with visual perception and temporal memory. Evaluation in a controlled emulation setting on 180 malware samples from three threat classes shows higher Activity Rates and Coverage, and shorter Time-to-Reveal than passive and fixed scripted baselines. These results support the feasibility of adaptive interactions as a promising direction for sandbox analysis, while broader external validation, matched comparisons with prior systems, and component-wise ablation remain future work. Full article

(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)

24 pages, 15962 KB

Open AccessArticle

Robust Controller Design for Delayed Load Frequency Control Systems Under Wind Power Uncertainty

by Yantao Lou, Tonghui Wang, Yilun Cai and Jing He

Electronics 2026, 15(11), 2347; https://doi.org/10.3390/electronics15112347 - 28 May 2026

Abstract

Wind power uncertainty can significantly deteriorate the frequency regulation performance and robustness of load frequency control (LFC) systems, particularly in the presence of communication delays. However, most existing studies rely on simplified wind power fluctuation models, which cannot adequately capture the segmented and [...] Read more.

Wind power uncertainty can significantly deteriorate the frequency regulation performance and robustness of load frequency control (LFC) systems, particularly in the presence of communication delays. However, most existing studies rely on simplified wind power fluctuation models, which cannot adequately capture the segmented and stochastic characteristics of wind speed variations. As a result, the resulting robustness analysis may deviate from practical operating conditions, leading to controller designs that are less reliable and less effective in real-world scenarios. To address this issue, this paper develops a robust controller co-design framework for delayed LFC systems under wind power uncertainty. First, a probabilistic wind power model with wind speed segmentation characteristics is established, and electric vehicles are incorporated into frequency regulation to construct a multi-area delayed LFC model. Then, a robust performance index is introduced to quantify disturbance rejection capability, and a genetic algorithm–particle swarm optimization-based collaborative optimization strategy is employed to determine controller parameters efficiently. Simulation results on both single-area and two-area LFC systems demonstrate that the proposed method achieves superior frequency regulation performance and stronger robustness against wind disturbances and time delays compared with designs that neglect wind uncertainty. Quantitatively, compared with controllers designed based on simplified wind power modeling, the proposed design framework reduces the normalized integral of time multiplied absolute value of the error (ITAE), integral of squared error (ISE), and integral of absolute error (IAE) indices by approximately 17.4% and 9% on average in the single-area and two-area cases, respectively. Full article

(This article belongs to the Section Systems & Control Engineering)

► Show Figures

Figure 1

35 pages, 9866 KB

Open AccessArticle

A Self-Powered, Fast-Response High-Voltage Safety Discharge Topology Based on Cascaded Depletion-Mode NMOS for Compact Pulse Generators

by Quanlin Li, Xinya Cheng, Yuan Ning, Heming Zhao and Yuxiao Wang

Electronics 2026, 15(11), 2346; https://doi.org/10.3390/electronics15112346 - 28 May 2026

Abstract

High-voltage short pulse generators play a critical role in medical and industrial applications. However, the presence of residual stored energy can pose significant electrical safety hazards. To mitigate these hazards, the implementation of rapid discharge mechanisms is imperative. To address the limitations of [...] Read more.

High-voltage short pulse generators play a critical role in medical and industrial applications. However, the presence of residual stored energy can pose significant electrical safety hazards. To mitigate these hazards, the implementation of rapid discharge mechanisms is imperative. To address the limitations of slow passive bleeders and auxiliary-dependent active circuits, and the issue of excessive size for compact pulse generators, this study proposes a self-powered, fast-response discharge topology utilizing cascaded depletion-mode NMOS transistors. The method utilizes the inherent normally-on characteristic of depletion-mode devices to ensure fail-safe activation during power loss, employing a self-biased feedback loop to regulate a constant discharge current. The theoretical models were validated through simulations and a hardware prototype testing a 1200 V/220 nF capacitor. The experimental results demonstrate the capability to successfully discharge 1200 V to a safe level within a span of one second. Additionally, the discharge time can be programmed within the range from 72 milliseconds to 1.02 s by adjusting the current-limiting resistor. In summary, the proposed topology offers a reliable, compact, and adjustable solution for high-voltage safety, addressing the limitations of conventional discharge technologies in terms of volume and speed. Full article

(This article belongs to the Section Power Electronics)

► Show Figures

Figure 1

29 pages, 595 KB

Open AccessArticle

A Hierarchical Bayesian Detector for Weak Underwater Acoustic Signal Detection Under Environmental Mismatch

by Yuhang Wang and Jing Lv

Electronics 2026, 15(11), 2345; https://doi.org/10.3390/electronics15112345 - 28 May 2026

Abstract

Weak underwater acoustic signal detection is fundamentally challenged by low signal-to-noise ratio (SNR), colored ocean noise, multipath distortion, and environmental mismatch. Existing weak-signal detectors have mainly focused on spectral enhancement, time-frequency tracking, or fixed-environment model matching, while environmentally robust Bayesian methods have been [...] Read more.

Weak underwater acoustic signal detection is fundamentally challenged by low signal-to-noise ratio (SNR), colored ocean noise, multipath distortion, and environmental mismatch. Existing weak-signal detectors have mainly focused on spectral enhancement, time-frequency tracking, or fixed-environment model matching, while environmentally robust Bayesian methods have been developed primarily for localization, matched-field processing, and channel estimation rather than weak passive detection itself. To bridge this gap, this paper proposes a hierarchical Bayesian detector for weak underwater acoustic signal detection under environmental mismatch. The received observation is modeled by jointly incorporating structured weak-signal coefficients, target-related parameters, and uncertain environmental parameters into a unified Bayesian hypothesis-testing framework. In particular, the acoustic environment is treated as a latent random variable rather than a fixed nominal condition so that robustness can be achieved through environmental marginalization. Since the resulting marginal likelihood is analytically intractable, a variational Bayesian approximation is developed to derive a tractable evidence-based detection statistic. Numerical simulations under low-SNR, multipath-distorted, and environmentally uncertain underwater conditions demonstrate that the proposed detector achieves consistently strong performance under both matched and mismatched scenarios. Ablation results in controlled simulations further indicate that environmental marginalization provides the largest observed robustness gain, whereas the structured weak-signal prior offers an additional improvement in weak-signal discrimination. These results provide controlled simulation-based evidence for the potential of hierarchical Bayesian inference in robust passive underwater acoustic detection under prescribed environmental uncertainty models. Full article

(This article belongs to the Section Microwave and Wireless Communications)

► Show Figures

Figure 1

40 pages, 8368 KB

Open AccessArticle

A Vertebra-Aware Framework for Structured Analysis of Post-Fracture Lumbar CT

by Zhe-Yu Ye, Jun-Mu Peng and Tamotsu Kamishima

Electronics 2026, 15(11), 2344; https://doi.org/10.3390/electronics15112344 - 28 May 2026

Abstract

Routine computed tomography (CT) provides an opportunity for opportunistic vertebra-aware analysis beyond its original acquisition purpose. In this work, we study the engineering feasibility of transforming routine post-fracture lumbar CT into a compact structured case summary, rather than producing only a single black-box [...] Read more.

Routine computed tomography (CT) provides an opportunity for opportunistic vertebra-aware analysis beyond its original acquisition purpose. In this work, we study the engineering feasibility of transforming routine post-fracture lumbar CT into a compact structured case summary, rather than producing only a single black-box prediction. We propose a vertebra-aware 3D multi-task learning framework that jointly performs vertebral segmentation, density-related descriptor estimation, CT- and geometry-derived structure-aware descriptor estimation, vertebra-level fracture-related auxiliary modeling, derived case-level summary generation, and quality-control/uncertainty-aware output organization. The structure-aware descriptor is introduced as a framework-defined quantitative field for organizing density-related signal distribution and vertebral geometry on the current scan, not as a validated biomechanical measurement or intrinsic-strength estimator. Experiments on xVertSeg using five-fold case-level cross-validation show that the framework can generate coherent vertebra-wise structured outputs and support preliminary derived case-level discriminative analysis under limited supervision. To partially address the small-sample limitation, supplementary experiments on VerSe 2020 are conducted for external anatomical generalization and anatomical pretraining. The results indicate that VerSe-based pretraining improves segmentation stability and downstream descriptor consistency after xVertSeg fine-tuning. Overall, this study should be interpreted as an engineering proof-of-concept for report-oriented structured analysis of post-fracture lumbar CT, rather than as prospective prediction, biomechanical validation, or a clinically deployed decision-support system. Full article

29 pages, 3713 KB

Open AccessArticle

CMFA-Net: A CNN–Mamba Collaborative Feature Alignment Network for Robust Medical Image Segmentation

by Liu Yang, Hui Wang, Xiaolin Fu, Yang Wang and Duohai Wu

Electronics 2026, 15(11), 2343; https://doi.org/10.3390/electronics15112343 - 28 May 2026

Abstract

Medical image segmentation still faces three critical challenges: insufficient joint modeling of local details and long-range dependencies, the high computational burden of transformer-based architectures for high-resolution inputs, and performance degradation caused by domain shift across imaging centers and acquisition devices. To address these [...] Read more.

Medical image segmentation still faces three critical challenges: insufficient joint modeling of local details and long-range dependencies, the high computational burden of transformer-based architectures for high-resolution inputs, and performance degradation caused by domain shift across imaging centers and acquisition devices. To address these issues, this paper proposes CMFA-Net, a CNN–Mamba collaborative feature alignment network for robust medical image segmentation. The proposed framework adopts Vision Mamba (VSSM) as the encoder backbone to capture long-range contextual dependencies with linear computational complexity. A CNN–Mamba fusion attention (CMFA) module is designed to integrate the local representation capability of convolution with the long-range modeling capability of Mamba, improving the segmentation of complex boundaries and multi-scale targets. In addition, an enhanced multi-scale context aggregation decoder (EMCAD) is introduced to reduce the semantic gap between encoder and decoder features and strengthen hierarchical feature fusion. To enhance cross-dataset robustness, a contrastive domain alignment learning (cDAL) strategy is applied in the intermediate feature space to learn domain-invariant discriminative representations via an InfoNCE-based objective. Experiments on the CirrMRI600+ pathological liver MRI dataset and several public polyp segmentation benchmarks show that the proposed method achieves competitive segmentation performance. Ablation studies provide empirical evidence for the contributions of the CMFA module, EMCAD decoder, and cDAL mechanism under the same experimental protocol. These results suggest that CMFA-Net is a promising framework for medical image segmentation across heterogeneous datasets. Full article

(This article belongs to the Special Issue AI-Driven Medical Image/Video Processing)

Journal Description

Electronics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI