Electronics

18 pages, 5776 KB

Open AccessArticle

BP-Neural-Network-Based Adaptive Parameter Control for Grid-Following Inverters with Frequency-Band-Coordinated Regulation

by Ming Li, Yaojie Luo, Jin Chen, Minghao Liu, Jianhang Zhang, Zhihong Xiang and Xing Zhang

Electronics 2026, 15(18), 4131; https://doi.org/10.3390/electronics15184131 - 11 Sep 2026

Abstract

The large-scale integration of renewable energy causes grid strength to vary over a wide range, exposing grid-following (GFL) inverters to both mid- and high-frequency resonance and subsynchronous oscillation (SSO). Conventional fixed-parameter designs cannot simultaneously maintain stability and dynamic performance because the phase-locked loop [...] Read more.

The large-scale integration of renewable energy causes grid strength to vary over a wide range, exposing grid-following (GFL) inverters to both mid- and high-frequency resonance and subsynchronous oscillation (SSO). Conventional fixed-parameter designs cannot simultaneously maintain stability and dynamic performance because the phase-locked loop (PLL) and grid-voltage feedforward (GVF) dominate different frequency bands. This paper therefore proposes a backpropagation-neural-network (BPNN)-based adaptive parameter control strategy with frequency-band-coordinated regulation. First, a q-axis small-signal output-admittance model incorporating the current loop, digital delay, PLL, and GVF is established. The model reveals that the GVF coefficient primarily shapes mid- and high-frequency admittance under strong and moderately weak grids, whereas the PLL bandwidth becomes the dominant factor in low-frequency and subsynchronous stability under ultra-weak grids. Based on this mechanism, a BPNN is constructed with the grid short-circuit ratio (SCR) as the input and the GVF coefficient and PLL bandwidth as the outputs. Training targets are generated offline using parameter sweeps and performance screening based on current total harmonic distortion, Point of Common Coupling (PCC) voltage error, and settling time. During operation, the GVF coefficient is adjusted first, and the PLL bandwidth is reduced only when the grid becomes ultra-weak. Simulation results over

SCR = 1.25

–10 demonstrate that the proposed strategy preserves stable operation while providing better transient and harmonic performance than fixed-parameter and single-parameter tuning schemes in the cases studied. Full article

(This article belongs to the Special Issue New Insights into Protection, Stability, and Security of Power System with High Penetration of Renewable Energy Sources)

34 pages, 6164 KB

Open AccessArticle

ZMP-Guided Ground Anchoring for Quadrotor Landings Using MRAC-Neural Networks

by Özgür Altınışık and Seta Bogosyan

Electronics 2026, 15(18), 4130; https://doi.org/10.3390/electronics15184130 - 11 Sep 2026

Abstract

Landing underactuated quadrotors on inclines is challenging as sudden contact transients convert translational kinetic energy into critical edge-tipping moments. Conventional controllers struggle to mitigate these sub-second impacts without computationally intensive prior terrain models or payload-restricting mechanical shock absorbers. This paper introduces a stabilization [...] Read more.

Landing underactuated quadrotors on inclines is challenging as sudden contact transients convert translational kinetic energy into critical edge-tipping moments. Conventional controllers struggle to mitigate these sub-second impacts without computationally intensive prior terrain models or payload-restricting mechanical shock absorbers. This paper introduces a stabilization approach combining a Zero Moment Point (ZMP)-guided anchoring strategy with an impact-resilient Model Reference Adaptive Control Neural Network (MRAC-NN). The ZMP serves as a proactive geometric threshold. Evaluating overturning moments directly in the body frame before physical tilt occurs triggers immediate asymmetric bidirectional thrust. This artificially augments the surface normal force, thereby securing the vehicle on steep inclines that exceed natural static friction limits. Concurrently, the computationally efficient MRAC-NN suppresses unstructured aerodynamic disturbances and high-frequency impact shocks. To prevent parameter wind-up during sudden kinematic arrest at touchdown, the neuro-adaptive law is structurally augmented with robust bounding and dynamic state-locking mechanisms, mathematically guaranteeing Uniform Ultimate Boundedness (UUB). The framework is validated through high-fidelity simulations that incorporate Linear Complementarity Problem (LCP) rigid-impact constraints and stick–slip friction. Results demonstrate the architecture effectively suppresses post-impact bouncing and transforms critical tipping moments into controlled planar slides, reducing stabilization penalties by up to

47.5 %

and expanding the survivable flight envelope to

60^{\circ}

inclines where classical methods fail. Full article

(This article belongs to the Special Issue Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 3rd Edition)

31 pages, 42922 KB

Open AccessArticle

Isoline Tracking Control of an Autonomous Underwater Helicopter in Unknown Marine Scalar Fields Under Noisy Measurements

by Yongkai Zang, Wuchen Kang and Zheyuan Wu

Electronics 2026, 15(18), 4129; https://doi.org/10.3390/electronics15184129 - 11 Sep 2026

Abstract

For continuous boundary observation at a prescribed scalar level in unknown marine scalar fields, this paper proposes an isoline tracking method for an autonomous underwater helicopter (AUH) using only noisy local measurements. The method establishes an isoline kinematic relation based on the local [...] Read more.

For continuous boundary observation at a prescribed scalar level in unknown marine scalar fields, this paper proposes an isoline tracking method for an autonomous underwater helicopter (AUH) using only noisy local measurements. The method establishes an isoline kinematic relation based on the local scalar value and its rate of change. Without field reconstruction or compensation for unknown terms, it enables the AUH to stably approach the target isoline and maintain tangential motion, with adjustable tracking accuracy. To obtain reliable scalar-rate information from noisy measurements, a finite-time differentiator using current and historical noisy measurements was developed to suppress measurement noise while retaining the required dynamic response. A low-complexity prescribed-time velocity-matching controller was further developed in a direct algebraic form, allowing the contraction time and terminal range of the velocity errors to be specified without imposing restrictive conditions on finite initial errors. Simulation results in a suspended particulate matter scalar field and a temperature field verified the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Robotics and Intelligent Control)

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22 pages, 4757 KB

Open AccessArticle

A Study on Urban Traffic Speed Prediction Using Spatiotemporal Attention-Based Convolutional Neural Networks

by Zhuye Xu, Shaoyang Wang, Hongqiang Xin and Zhongdong Hua

Electronics 2026, 15(18), 4128; https://doi.org/10.3390/electronics15184128 - 11 Sep 2026

Abstract

Urban traffic speed prediction requires effective modeling of road-network spatial dependencies and temporal dynamics. This study develops an attention-based spatiotemporal graph convolutional framework (AT-GCN-GRU) for multi-horizon traffic speed prediction. The model combines graph convolution for spatial feature extraction, GRU for temporal modeling, and [...] Read more.

Urban traffic speed prediction requires effective modeling of road-network spatial dependencies and temporal dynamics. This study develops an attention-based spatiotemporal graph convolutional framework (AT-GCN-GRU) for multi-horizon traffic speed prediction. The model combines graph convolution for spatial feature extraction, GRU for temporal modeling, and a temporal attention mechanism for adaptively weighting historical hidden states. Experiments were conducted on the SZ-taxi dataset and further evaluated on METR-LA. On SZ-taxi, AT-GCN-GRU was compared with conventional and modern baselines, including T-GCN, ASTGCN, and AGCRN, over 15, 30, 45, and 60 min forecasting horizons. The proposed model remained competitive with the modern baselines and obtained lower mean RMSE than T-GCN across the four evaluated horizons, with relative reductions of approximately 1.4–2.9%. Additional ablation, missing-data robustness, attention-weight visualization, and computational-cost analyses were conducted. The results suggest that temporal attention can provide incremental improvements to the GCN-GRU framework with relatively limited additional computational overhead. Full article

(This article belongs to the Section Artificial Intelligence)

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33 pages, 829 KB

Open AccessArticle

Hardware-Aware Acceleration of Open-Vocabulary Multi-Object Navigation on Edge GPUs

by Michael Chibudom Akor and Heoncheol Lee

Electronics 2026, 15(18), 4127; https://doi.org/10.3390/electronics15184127 - 11 Sep 2026

Abstract

Persistent open-vocabulary navigation enables robots to search for sequential language-specified objects using reusable visual semantic evidence. On edge GPUs, the pipeline is constrained by foundation-model inference, semantic projection, persistent map movement, frontier processing, and repeated target detection. Using OneMap as a controlled reference, [...] Read more.

Persistent open-vocabulary navigation enables robots to search for sequential language-specified objects using reusable visual semantic evidence. On edge GPUs, the pipeline is constrained by foundation-model inference, semantic projection, persistent map movement, frontier processing, and repeated target detection. Using OneMap as a controlled reference, we reorganize this workload through compiled mixed-precision perception, direct-to-map confidence-weighted feature aggregation, accelerator-resident semantic maps, compiled navigation operations, and language-conditioned detector scheduling. The resulting dataflow removes the dense image resolution feature intermediate and schedules target confirmation detection from current-view target similarity. On the complete Habitat-Matterport 3D benchmarks, our implementation retains 92.6% and 96.1% of the reproduced OneMap single- and multi-object success rates. Across 36 paired hardware-in-the-loop comparisons on a Jetson AGX Orin at four power modes, it achieves geometric mean speedups of 4.64× for on-device computation and 3.55× for the complete episode duration. It also reduces peak system random access memory (RAM) by 51.21% and a summed board-rail energy proxy by 75.32% on average while preserving every paired outcome. These results establish the coordinated dataflow and current-view target-conditioned execution as effective mechanisms for efficient persistent foundation-model navigation on power-constrained edge robots. Full article

(This article belongs to the Special Issue Recent Advances in AI Hardware Design)

32 pages, 4170 KB

Open AccessArticle

Multimodal Diagnostic Overlays for Tabular Deep Learning: Fractal, Spectral, and Reflex-Aware Interpretability

by Helen O’Brien Quinn and Mohamed Sedky

Electronics 2026, 15(18), 4126; https://doi.org/10.3390/electronics15184126 - 11 Sep 2026

Abstract

Deep learning models for tabular data often behave unpredictably during training, and these internal dynamics directly influence the reliability of post hoc interpretability methods. Static techniques such as SHapley Additive exPlanations (SHAP) and Integrated Gradients offer useful but incomplete snapshots of model behaviour, [...] Read more.

Deep learning models for tabular data often behave unpredictably during training, and these internal dynamics directly influence the reliability of post hoc interpretability methods. Static techniques such as SHapley Additive exPlanations (SHAP) and Integrated Gradients offer useful but incomplete snapshots of model behaviour, while tabular models frequently undergo noise-driven fluctuations, structural reorganisations, and collapse phases that these methods cannot detect. This study introduces a temporally aligned, multimodal diagnostic framework that observes and interprets model behaviour as it evolves. The framework integrates fractal complexity, spectral energy, persistent homology, gradient-noise statistics, reflex responsiveness, attribution instability, and exploratory sonification into a single phase-aligned system. The framework is evaluated on Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) models across benchmark and real-world datasets using a reproducible protocol with consistent configurations and autocorrelation-robust analysis. An ablation study confirms the distinct diagnostic value of each modality, with reflex-aware metrics validated against optimiser behaviour and sonification assessed through event detection. The multimodal diagnostics reveal instability regimes, fragmentation events, attribution drift, and optimiser–noise coupling failures that static Explainable Artificial Intelligence (XAI) methods cannot detect. GRU models show repeated structural transitions, whereas MLPs exhibit simpler but noisier optimisation dynamics. These behaviour-aware, temporally aligned diagnostics provide a more reliable foundation for interpreting tabular deep learning models. Full article

(This article belongs to the Special Issue Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy)

20 pages, 11303 KB

Open AccessArticle

Vision-Based Autonomous System for Counting and Inventory Monitoring of Metal Profiles

by Kinga Bettina Faragó, Gyöngyvér Ferencz, Marcell Pólik, Anna Tüske and Ellák Somfai

Electronics 2026, 15(18), 4125; https://doi.org/10.3390/electronics15184125 - 11 Sep 2026

Abstract

Maintaining up-to-date inventory data is essential for modern supply chains, yet transitioning to automated tracking frequently requires prohibitive structural modifications and substantial financial investments. To address this bottleneck and enable low-cost innovation within existing legacy warehouse infrastructures, we developed an industrially deployable, practical [...] Read more.

Maintaining up-to-date inventory data is essential for modern supply chains, yet transitioning to automated tracking frequently requires prohibitive structural modifications and substantial financial investments. To address this bottleneck and enable low-cost innovation within existing legacy warehouse infrastructures, we developed an industrially deployable, practical vision system that integrates deep learning, classical computer vision, and robotic elements, which we call the MOBOT system. Its vision pipeline unifies robotic image capture, depth-based ROI localization, perspective transformation, material identification, and lightweight geometric counting algorithms into an adaptive, field-ready framework. We evaluated this inventory-monitoring system in an operational warehouse storing semi-finished metal products. Experimental results confirm the system’s high robustness across various geometries: structured hollow shapes, such as tubes, hollow sections, and U-profiles, result in a low error rate of 2–4%, while for irregular, solid cross-sections, the fundamental error rate measured under real-world conditions is 7–20%. This robot-assisted platform delivers unambiguous semi-finished metal product inventory monitoring without requiring facility modifications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Advances and Applications)

23 pages, 13421 KB

Open AccessArticle

A Low-Power 32.768 kHz Multi-Crystal Oscillator for General-Purpose Microcontrollers

by Marine Brun, Gilles Jacquemod, Yoann Charlon and Philippe Le Fevre

Electronics 2026, 15(18), 4124; https://doi.org/10.3390/electronics15184124 - 11 Sep 2026

Abstract

This paper presents a low-power 32.768 kHz multi-crystal oscillator implemented in advanced 18 nm CMOS technology and integrated into a general-purpose microcontroller (MCU) environment. The oscillator operates over a wide supply voltage range from 1.6 V to 3.6 V and across a temperature [...] Read more.

This paper presents a low-power 32.768 kHz multi-crystal oscillator implemented in advanced 18 nm CMOS technology and integrated into a general-purpose microcontroller (MCU) environment. The oscillator operates over a wide supply voltage range from 1.6 V to 3.6 V and across a temperature range from

- 40^{\circ} C

to

130^{\circ} C

, meeting industrial requirements. The experimental results demonstrate a current consumption below 200 nA under typical conditions (i.e., 3 V and

25^{\circ} C

), with reliable start-up and stable oscillations using various crystals that cover a broad range of crystal market characteristics. The oscillator supports crystals with equivalent series resistance (ESR) values up to

100 k Ω

and datasheet load capacitances ranging from

4 pF

to

12.5 pF

without amplifier reconfiguration. The design is based on a two-stage amplifier architecture that effectively reduces power consumption while improving frequency stability and predictability. The simulations show that the proposed architecture offers greater robustness than the classical Pierce architecture regarding oscillation frequency variations caused by potential crystal ESR fluctuations. These findings are validated by measurements, which exhibit a high correlation between the simulated and measured oscillation impedance behavior. Full article

(This article belongs to the Section Circuit and Signal Processing)

20 pages, 2193 KB

Open AccessArticle

A LiDAR-Based Multimodal 3D Object Detection Algorithm for Intelligent Driving in Open-Pit Mines

by Shuqi Wang and Xinyi Zhang

Electronics 2026, 15(18), 4123; https://doi.org/10.3390/electronics15184123 - 11 Sep 2026

Abstract

This study addresses the challenges of sparse long-range point clouds, complex background interference, and inconsistent localization quality in 3D object detection for intelligent driving in open-pit mines. A camera–LiDAR multimodal detection method based on Voxel R-CNN is proposed. We introduce a Multimodal Focal [...] Read more.

This study addresses the challenges of sparse long-range point clouds, complex background interference, and inconsistent localization quality in 3D object detection for intelligent driving in open-pit mines. A camera–LiDAR multimodal detection method based on Voxel R-CNN is proposed. We introduce a Multimodal Focal Sparse Voxel Enhancement module that combines Focal Sparse Convolution with shallow visual features to guide voxel importance prediction and selective sparse propagation. An Intersection over Union (IoU)-Aware Quality and Geometry Refinement Head is further designed to improve the localization accuracy and ranking reliability of 3D proposals. Experimental results show that the proposed method achieves BEV mAP@0.40 and 3D mAP@0.40 values of 63.43% and 61.23%, respectively, outperforming the strongest comparison method, MambaFusion, by 5.37 and 7.61 percentage points. In the 60–80 m range, the average translation error is reduced to 0.41 m, while the inference speed reaches 27 FPS. These results demonstrate that the proposed method improves the detection and localization of distant sparse objects in complex open-pit mine environments while maintaining real-time inference capability. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

32 pages, 4231 KB

Open AccessArticle

Robust Closed-Loop Control of Industrial Systems Based on Cloud–Edge Device Collaboration

by Wenjing Zhang, Wenchao Zhang, Xiao Ma, Weijia Han, Liang Wang and Minghang Chen

Electronics 2026, 15(18), 4122; https://doi.org/10.3390/electronics15184122 - 11 Sep 2026

Abstract

Under China’s dual-carbon strategic goals, large-scale public centralized heating plays a critical role in energy conservation. However, traditional manual open-loop control suffers from high response latency. Furthermore, existing unidirectional predictive methods lack dynamic feedback correction mechanisms. To address these issues, this study proposes [...] Read more.

Under China’s dual-carbon strategic goals, large-scale public centralized heating plays a critical role in energy conservation. However, traditional manual open-loop control suffers from high response latency. Furthermore, existing unidirectional predictive methods lack dynamic feedback correction mechanisms. To address these issues, this study proposes an intelligent dual-system collaborative control architecture specifically designed for public heating systems. This architecture utilizes a cloud–edge device framework. It establishes a two-way linkage between heating equipment and the cloud decision platform. Consequently, it constructs an integrated regulation framework encompassing forward decision generation, reverse state verification, and dynamic feedback correction. Specifically, the forward module utilizes a Mamba-structured state-space model to generate data-driven boiler operation strategies. Meanwhile, the reverse module employs a Temporal Convolutional Network with Monte Carlo Dropout (TCN-MC Dropout). This probabilistic network enables state inversion evaluation with reliable uncertainty prediction intervals. These two modules are deeply coupled through an adaptive feedback correction mechanism. Together, they significantly improve system stability and operational robustness under complex thermal disturbances. Specifically, the proposed architecture achieves a room temperature compliance rate exceeding 96% and restricts temperature fluctuations to within

\pm 0.75 ° C

. Simultaneously, it reduces boiler energy consumption by over 16.4%. This solution has been successfully deployed in the heating network at Shaanxi Normal University as a representative real-world case study. Ultimately, it provides a practical technical reference for the intelligent upgrading and low-carbon transformation of public centralized heating systems. Full article

(This article belongs to the Special Issue Robustness and Security in Machine Learning Systems)

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25 pages, 1829 KB

Open AccessArticle

Adaptive Multi-Objective Beamforming and Power Allocation for MIMO-ISAC in Low-Altitude Wireless Networks

by Bing Yang, Yan Huo, Xin Fan and Chang Wang

Electronics 2026, 15(18), 4121; https://doi.org/10.3390/electronics15184121 - 11 Sep 2026

Abstract

Low-altitude wireless networks (LAWNs) require reliable multi-user communication together with accurate range and velocity sensing. Communication and sensing share the same transmit power and spatial degrees of freedom (DoF), and therefore joint beamforming is required to coordinate multi-user spectral efficiency with delay-Doppler estimation [...] Read more.

Low-altitude wireless networks (LAWNs) require reliable multi-user communication together with accurate range and velocity sensing. Communication and sensing share the same transmit power and spatial degrees of freedom (DoF), and therefore joint beamforming is required to coordinate multi-user spectral efficiency with delay-Doppler estimation accuracy. An adaptive multi-objective beamforming and power allocation framework is developed for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) base station. Communication performance is measured by the achievable multi-user sum spectral efficiency. Sensing performance is characterized by the Cramér–Rao lower bounds (CRLBs) for delay and Doppler frequency. A dimensionless system effectiveness integrated metric (SEIM) combines the three normalized performance components. The beamforming problem is lifted to transmit covariance matrices and treated via semidefinite relaxation (SDR) and alternating successive convex approximation (SCA) under power and per-user signal-to-interference-plus-noise ratio (SINR) constraints. An entropy-regularized weight subproblem provides a closed-form softmax update, and a damping step couples the weight update with the covariance iterations. Numerical results characterize the communication–sensing tradeoff with respect to the transmit power, array size, user loading, SINR requirements, and objective weights. Full article

(This article belongs to the Special Issue Communication Systems in Unmanned Aerial Vehicles)

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15 pages, 5393 KB

Open AccessArticle

Character-Level Visual Guidance for Arbitrarily Shaped Scene Text Detection and Recognition

by Lijia Chen, Hu Lin and Dan Chen

Electronics 2026, 15(18), 4120; https://doi.org/10.3390/electronics15184120 - 11 Sep 2026

Abstract

Curved, blurred, low-contrast, and cluttered scene text remains difficult to localize and transcribe because visual boundaries and character identities are often ambiguous. Most detectors refine spatial regions from visual and positional features, while many recognizers depend on implicit attention between global image tokens [...] Read more.

Curved, blurred, low-contrast, and cluttered scene text remains difficult to localize and transcribe because visual boundaries and character identities are often ambiguous. Most detectors refine spatial regions from visual and positional features, while many recognizers depend on implicit attention between global image tokens and language context. This separation weakens character-level evidence in both localization and text decoding. To address this issue, this paper studies explicit character-level guidance for scene text detection and recognition. For arbitrary-shaped text detection, the character-guided adaptive detector (CADet) combines a text-enhancement network (TENet), a character information adaptive guidance module (CIA), and a position and classification compensation module (COMP). Then, character semantics can participate in boundary-query construction before final detection. For scene text recognition (STR), serialized image embeddings for text recognition (SIETR) introduce local visual embeddings into autoregressive decoding via the character local image embedding module (CLIE) and permutation language modeling (PLM). On ArT, Total-Text, and CTW1500, CADet obtains F-measures of 79.5%, 89.4%, and 89.2%, respectively, outperforming representative Transformer-based detectors with only a small increase in computation. For recognition, SIETR reaches 95.6% sample-size-weighted average accuracy with 23.8 M parameters and 3.2 G FLOPs and improves most irregular-text benchmarks over PARSeq with fewer FLOPs. The results indicate that character semantics for detection and local visual evidence for decoding are an effective way to improve robustness in difficult scene text. Full article

(This article belongs to the Special Issue Visual Text Recognition and Understanding)

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21 pages, 5250 KB

Open AccessArticle

A Transient-Minimized DC Fault Protection with Z-Source Circuit Breakers in Hybrid Microgrids

by Ruiyun Fu and Kusum Neupane

Electronics 2026, 15(18), 4119; https://doi.org/10.3390/electronics15184119 - 11 Sep 2026

Abstract

With the growth of emerging data centers and electric vehicles, DC power distribution and hybrid microgrids are attracting growing attention. Due to the lack of a natural zero-crossing point in DC current, reliable DC fault protection is critical. This paper introduces a transient-minimized [...] Read more.

With the growth of emerging data centers and electric vehicles, DC power distribution and hybrid microgrids are attracting growing attention. Due to the lack of a natural zero-crossing point in DC current, reliable DC fault protection is critical. This paper introduces a transient-minimized fault protection scheme utilizing Z-source circuit breakers (ZCBs), leveraging their unique features during the activation process. These features are analyzed to provide the theoretical foundation for achieving transient-minimized DC fault protection, which eliminates high spikes in fault current, relieves stress on power devices in the system, and enhances transient stability. The effectiveness of this method is validated through simulation tests of a 100 MVA islanded hybrid microgrid using ZCBs based on experimentally verified models. Test results demonstrate that system oscillation is reduced by 13% in frequency and 74% in voltage, while completely eliminating voltage sags. With the help of ZCBs, the system also recovers more quickly after fault clearance. The results also demonstrate that the proposed method remains effective under various fault and load conditions. Full article

(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing, 2nd Edition)

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23 pages, 10060 KB

Open AccessArticle

A Dual-Path IoT Sensing and Communication Framework for Smart Building and Construction-Site Structural Monitoring

by Chia-Hau Chen, Yi-Hsuan Hsu, Wei-Lin Lee, Hock-Kiet Wong, Eric Hsiao-Kuang Wu, Shih-Ching Yeh and Tipajin Thaipisutikul

Electronics 2026, 15(18), 4118; https://doi.org/10.3390/electronics15184118 - 11 Sep 2026

Abstract

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication [...] Read more.

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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23 pages, 2170 KB

Open AccessArticle

PowerXGraph++: Regulation-Constrained Spatiotemporal Evidence Graphs with Counterfactual Explanations for Power-Worksite Safety Monitoring

by Qizhe Zhang, Yi Zhang, Zhengwei Chang, Zheng Wang and Guozheng Peng

Electronics 2026, 15(18), 4117; https://doi.org/10.3390/electronics15184117 - 11 Sep 2026

Abstract

Automated power-worksite monitoring requires accurate event recognition and auditable explanations. This paper presents PowerXGraph++, a regulation-constrained spatiotemporal evidence-graph framework for missing PPE, boundary crossing, unsafe proximity, falls, and fire/smoke. Typed evidence tokens are encoded by a transformer; full-context prediction is separated from explanation [...] Read more.

Automated power-worksite monitoring requires accurate event recognition and auditable explanations. This paper presents PowerXGraph++, a regulation-constrained spatiotemporal evidence-graph framework for missing PPE, boundary crossing, unsafe proximity, falls, and fire/smoke. Typed evidence tokens are encoded by a transformer; full-context prediction is separated from explanation selection, and explanations are tested for sufficiency, necessity, sparsity, and regulation consistency. Distillation, event-boundary counterfactuals, background-invariance training, and non-inferiority checkpoint selection preserve predictive performance. On annotation-derived construction-PPE graphs, three-seed Macro-F1, violation recall, and rationale F1 were 0.989, 0.965, and 0.901. Under the same frozen predictor, the supervised, grammar-aware selector attained rationale F1 of 0.983 and regulation consistency of 1.000, although the post hoc baselines lacked equivalent supervision. A single YOLO reproduction run using seed 17 on 197 matched person graphs yielded Macro-F1 of 0.844, missing-PPE recall of 0.591, and rationale F1 of 0.660. Expert evaluation produced Fleiss’ kappa of 0.740 and method-to-majority rationale F1 of 0.870. Held-out-camera reasoning and a two-camera fence-crossing pilot provide additional evidence under supplied-evidence conditions; the results do not establish automatic generation of every semantic predicate or unrestricted raw video performance. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Graph Neural Networks)

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16 pages, 7316 KB

Open AccessArticle

OCMS-D: A Sparsity-Constrained Modal Orthogonality Method for Single-Frequency Source Depth Estimation with a Vertical Array

by Wei Gao, Yangjin Xu and Guocheng Gao

Electronics 2026, 15(18), 4116; https://doi.org/10.3390/electronics15184116 - 11 Sep 2026

Abstract

To address the challenge of estimating the depth of a single-frequency (tonal) source using a vertical line array (VLA) without prior knowledge of seabed parameters, a method called OCMS-D (orthogonality-constrained modal search-based depth estimation) is presented. The method exploits the orthogonality of mode [...] Read more.

To address the challenge of estimating the depth of a single-frequency (tonal) source using a vertical line array (VLA) without prior knowledge of seabed parameters, a method called OCMS-D (orthogonality-constrained modal search-based depth estimation) is presented. The method exploits the orthogonality of mode depth functions as a physical constraint and leverages the sparsity of propagating normal modes in the received field. A convex optimization model is formulated to jointly estimate modal wavenumbers, depth functions, and complex mode amplitudes. Furthermore, a depth-sign search (DSS) is introduced to compensate for mode phase signs, which effectively suppresses sidelobes in the depth ambiguity function and enables high-accuracy depth estimation. Numerical simulations analyze the influence of signal-to-noise ratio, array parameters and sound speed profile (SSP) uncertainty on the algorithm’s performance. Comparisons with matched-mode processing (MMP) show that OCMS-D exhibits superior robustness to SSP uncertainty and requires a smaller array aperture. Experimental data from the SWellEx-96 campaign demonstrate that the method achieves depth estimation errors of less than 5.4 m for the shallows and less than 10.8 m for the deep spurce. Notably, it requires neither seabed prior information nor source motion, offering a robust array signal processing framework for tonal source localization. Full article

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3 pages, 145 KB

Open AccessCorrection

Correction: Chen et al. Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation. Electronics 2021, 10, 2183

by Chengcheng Chen, Xianchang Wang, Huiling Chen, Chengwen Wu, Majdi Mafarja and Hamza Turabieh

Electronics 2026, 15(18), 4115; https://doi.org/10.3390/electronics15184115 - 11 Sep 2026

Abstract

In the original publication [...] Full article

13 pages, 985 KB

Open AccessArticle

FCA-Transformer: A Feature Pyramid Time Series Forecasting Model Driven by Cross-Attention Mechanism

by Linli Wu, Jiyong Zhang, Zhimin Zhang, Weiwei Cao, Yu Jiao and Zhangyi Shen

Electronics 2026, 15(18), 4114; https://doi.org/10.3390/electronics15184114 - 10 Sep 2026

Abstract

Multivariate time series forecasting requires modeling both hierarchical temporal dynamics and complex inter-variable dependencies, a dual requirement that often degrades predictive performance and incurs high computational costs in standard Transformer architectures. Unlike current channel-independent models that ignore vital cross-variable synergies, or dense-attention frameworks [...] Read more.

Multivariate time series forecasting requires modeling both hierarchical temporal dynamics and complex inter-variable dependencies, a dual requirement that often degrades predictive performance and incurs high computational costs in standard Transformer architectures. Unlike current channel-independent models that ignore vital cross-variable synergies, or dense-attention frameworks that suffer from quadratic computational noise, our approach extracts structurally sparse dependencies. To address these specific limitations, this study introduces the FCA-Transformer. The proposed framework integrates a Feature Pyramid Network (FPN) to isolate macroscopic trends from high-frequency localized fluctuations via hierarchical downsampling. Concurrently, a structured Transformer-based Cross-Attention (TCA) mechanism employs Dimensional Segmentation with Weighting (DSW) and a Two-Stage Attention (TSA) layer to map topological variable interactions, effectively extracting robust cross-variable pathways and mitigating distributional noise. Extensive empirical evaluations across three real-world multivariate benchmarks (ETTh1, Electricity, and Exchange Rate) demonstrate that the FCA-Transformer achieves an average reduction of up to 4.39% in MSE and 5.11% in MAE compared to leading baselines. These findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments. Full article

(This article belongs to the Section Artificial Intelligence)

35 pages, 5750 KB

Open AccessReview

A Survey of Optimal Resource Allocation in Semantic Communications: Technologies, Development Trends, and Applications

by Jiaqi Liu, Chang Guo, Wei Gao, Zhenyi Wang, Zhen Li, Kai Li and Jungang Yang

Electronics 2026, 15(18), 4113; https://doi.org/10.3390/electronics15184113 - 10 Sep 2026

Abstract

As an emerging communication paradigm, semantic communications (SC) focuses on the semantic content of information transmission, aiming to achieve more efficient and accurate information interaction. However, SC requires an in-depth analysis of information semantics and accurate adaptation to application scenarios. With the substantial [...] Read more.

As an emerging communication paradigm, semantic communications (SC) focuses on the semantic content of information transmission, aiming to achieve more efficient and accurate information interaction. However, SC requires an in-depth analysis of information semantics and accurate adaptation to application scenarios. With the substantial growth in information volume, richness, and application diversity, the resources, constraints, and requirements associated with SC systems have also increased. Optimal resource allocation (ORA) of SC can effectively deal with these practical problems, a key technology for improving communication efficiency. In complex network environments and special scenarios, communication resources are limited. Increasing transmission efficiency, ensuring accuracy and quality of information, and reducing energy consumption can be achieved by rationally allocating bandwidth, power, and related resources. Since SC technology is still in its early stages of development, there is a lack of a comprehensive review of ORA for SC in the existing literature. This paper provides a comprehensive review of ORA for SC. First, the basic concepts, characteristics and development motivations for SC and ORA are reviewed. Then, a comprehensive analysis of the key technologies for ORA in SC is presented, covering end-to-end and semantic network multi-link ORA. These technologies include technology for predicting resource demand based on semantic understanding, resource optimization technology for semantic information (SI) processing and transmission, and technology for dynamic resource adjustment. Then, a conceptual ORA framework is synthesized from the reviewed technologies to unify key design principles and provide a foundation for future research. In addition, this paper provides research prospects in future trends of ORA for SC, comprising emerging artificial intelligence (AI) and machine learning, laying the foundation for next-generation intelligent communication networks. Finally, this paper points out the main application direction of ORA in SC, which reflects the practical significance of this study. Full article

(This article belongs to the Special Issue Multimodal Sensing and Communications for B5G/6G Systems)

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