Electronics

29 pages, 2178 KB

Open AccessArticle

Runtime-Controlled Evaluation of Speculative Decoding Methods

by Sungkyun Kim, Jaemin Kim, Yeongpil Cho and Jiwon Seo

Electronics 2026, 15(14), 3179; https://doi.org/10.3390/electronics15143179 (registering DOI) - 20 Jul 2026

Reported Speculative decoding (SD) speedups are difficult to compare because methods are commonly evaluated with different serving runtimes and configurations. We present SpecLLM, a pluggable evaluation framework that applies common scheduling, batching, KV-cache, CUDA Graph, and execution-backend policies across methods while preserving method-required [...] Read more.

Reported Speculative decoding (SD) speedups are difficult to compare because methods are commonly evaluated with different serving runtimes and configurations. We present SpecLLM, a pluggable evaluation framework that applies common scheduling, batching, KV-cache, CUDA Graph, and execution-backend policies across methods while preserving method-required proposal and verification paths. We implement eight Speculative Decoding (SD) methods and evaluate them across batch sizes, controlled online loads, and two model scales. We decompose throughput speedup as

S = τ / c

, where

τ

is the average number of committed tokens per SD step and c is the dimensionless execution cost of that step relative to a matched autoregressive decode step. The measured c is conditional on the method–runtime interaction, hardware, workload, baseline, and configuration. Under the evaluated conditions, method rankings change with batch size and online load, and a longer commit length does not necessarily yield lower latency when execution cost or queueing increases. SpecLLM therefore provides controlled within-runtime comparability rather than runtime-independent fairness; SD results should report

(τ, c)

, latency, feasibility, and the conditions under which they were measured. Full article

(This article belongs to the Section Computer Science & Engineering)

► Show Figures

Figure 1

42 pages, 5569 KB

Open AccessReview

A Fairness Perspective on Client Selection and Aggregation Methods for Non-IID Mitigation in Federated Learning: A Survey

by Mohannad Alsofyani, Isra Alturaiki and Hassan Mathkour

Electronics 2026, 15(14), 3178; https://doi.org/10.3390/electronics15143178 (registering DOI) - 20 Jul 2026

Abstract

Federated learning (FL) is a promising approach for training distributed machine learning models while preserving clients’ data privacy. However, in real-world FL systems, data are often not independent and identically distributed (non-IID). This heterogeneity can slow convergence, degrade model performance, and increase client [...] Read more.

Federated learning (FL) is a promising approach for training distributed machine learning models while preserving clients’ data privacy. However, in real-world FL systems, data are often not independent and identically distributed (non-IID). This heterogeneity can slow convergence, degrade model performance, and increase client drift. To address these challenges, numerous methods have been proposed to mitigate non-IID data effects by optimizing client selection, local training, and model aggregation strategies. Despite their effectiveness in improving performance and efficiency, these methods rarely consider fairness across clients. Improving global accuracy does not guarantee balanced participation, influence, or outcomes, which may lead to biased model behavior across clients. In this survey, we review existing non-IID mitigation methods in FL from a fairness perspective and provide a systematic analysis of their implicit impact on client participation and influence. Unlike prior surveys that treat fairness as a separate research direction, this work analyzes how these methods designed for non-IID mitigation implicitly shape fairness outcomes across clients. Our taxonomy classifies existing methods into three categories—fairness-aware, semi-fairness-aware, and fairness-unaware—based on their design strategies for client selection and model aggregation. Using this taxonomy, we analyze the advantages, trade-offs, and limitations of each category and highlight that mitigating non-IID data does not guarantee fairness across clients. Finally, we identify open challenges and outline future directions, including system-level FL design that jointly considers non-IID mitigation and fairness and the development of standardized fairness evaluation metrics. Overall, this survey aims to provide a structured perspective on the relationship between non-IID mitigation and fairness and support the development of more balanced and scalable FL systems under non-IID conditions. Full article

(This article belongs to the Special Issue Federated Learning and Its Application)

► Show Figures

Figure 1

4 pages, 144 KB

Open AccessEditorial

Deep Learning Approaches for Natural Language Processing

by Aleksandra Świetlicka, Aleksandra Kawala-Sterniuk and Dariusz Mikołajewski

Electronics 2026, 15(14), 3177; https://doi.org/10.3390/electronics15143177 - 20 Jul 2026

Abstract

Deep learning (DL) has revolutionized natural language processing (NLP), enabling models to learn complex linguistic patterns directly from large-scale text data [...] Full article

(This article belongs to the Special Issue Deep Learning Approaches for Natural Language Processing)

17 pages, 3213 KB

Open AccessArticle

A Hybrid Address Normalization Framework Using Fine-Tuned LLMs to Enhance Geospatial Intelligence in Cancer Registries

by Ricardo Timarán Pereira, Jonathan Viveros Córdoba, Arsenio Hidalgo Troya, Luisa Bravo Goyes, Anívar Chaves Torres, Fredy Vidal Alegría, Andres Oswaldo Calderon Romero and Ginna Leyton Yela

Electronics 2026, 15(14), 3176; https://doi.org/10.3390/electronics15143176 - 20 Jul 2026

Abstract

The Population-based Cancer Registry of the Municipality of Pasto (RPCMP) faces significant challenges in spatial data integrity due to the high variability, inconsistency, and ambiguity of manually collected addresses. Such limitations restrict the analytical capacity of the YACHAY-GIS platform to generate precise geospatial [...] Read more.

The Population-based Cancer Registry of the Municipality of Pasto (RPCMP) faces significant challenges in spatial data integrity due to the high variability, inconsistency, and ambiguity of manually collected addresses. Such limitations restrict the analytical capacity of the YACHAY-GIS platform to generate precise geospatial intelligence across multiscalar levels. To address these issues, this paper proposes a novel hybrid address normalization framework structured in two sequential stages. First, a specialized deterministic geocoding engine, optimized for local urban morphology, is utilized. Second, for highly non-standard records—characterized by “block and lot” (manzana y lote) nomenclatures—a workflow based on Large Language Models (LLMs) is integrated. To ensure computational efficiency in resource-constrained environments, a Supervised Fine-Tuning (SFT) process was implemented using the Low-Rank Adaptation (LoRA) technique across various open-source LLMs. Experimental results demonstrate that the workflow integrated with the Llama-3.2-3B-Instruct model achieved the highest performance, with a normalization accuracy of 99.40%. The end-to-end evaluation shows an increase in spatial recovery from a 33.02% baseline to 55.13%, effectively isolating the semantic gap from the underlying cartographic limitations of the region. This architecture provides a scalable data engineering model for oncological registries, with future work focused on integrating external spatial providers such as OpenStreetMap and Colombia’s Agustín Codazzi Geographic Institute (IGAC) to further enhance geographic coverage. Full article

(This article belongs to the Special Issue Advanced Information Systems: Novel Techniques and Data-Driven Solutions)

► Show Figures

Figure 1

73 pages, 5362 KB

Open AccessArticle

Heterogeneous Low-Power Agentic Architecture for Edge Distributed Home Surveillance

by Luigi Lucio Zappia, Marco Lattuada, Marco Bianchessi and Danilo Pietro Pau

Electronics 2026, 15(14), 3175; https://doi.org/10.3390/electronics15143175 - 19 Jul 2026

Abstract

Recent advancements in edge hardware enabled increasingly complex artificial intelligence workloads to be executed directly on resource-constrained devices. However, single-edge devices remain computationally limited compared with hybrid or cloud alternatives. This work presents a fully edge-based architecture implementing agentic AI on heterogeneous devices [...] Read more.

Recent advancements in edge hardware enabled increasingly complex artificial intelligence workloads to be executed directly on resource-constrained devices. However, single-edge devices remain computationally limited compared with hybrid or cloud alternatives. This work presents a fully edge-based architecture implementing agentic AI on heterogeneous devices for home surveillance with a natural language interface, in which analysis tasks are parallelized across the distributed devices. The system is tested on a two-node prototype: a Raspberry Pi 5 with a Hailo-10H accelerator and an STM32MP257FDK board. Dynamic leader election allows recovery from the loss of any single node, including the leader, with graceful degradation. Qwen3-1.7B achieved 93% tool-selection accuracy over 24 tools and maintained robust summarization performance, outperforming FunctionGemma-270M. The jina-reranker-v2-base-multilingual cross-encoder performs comparably to the tested LLMs at tool selection on small pool sizes (i.e., with few tools to choose from). Hardware accelerators improved inference energy efficiency by 2.0x to 17.3x, while leader execution incurred less than 2 W of additional power. The main bottleneck is model latency rather than network overhead, and KV-cache reuse proved essential to meet the latency constraint on the STM32MP257FDK. The proposed prototype coordinates inference across heterogeneous edge devices to answer queries, with the weaker node exhibiting scalability limitations. Full article

22 pages, 3026 KB

Open AccessArticle

TA-STGAT: A Spatio-Temporal Graph Attention Network for Edge-State Prediction in Vehicular Edge Computing

by Qiong Shi, Wenwen Cheng and Mengli Wang

Electronics 2026, 15(14), 3174; https://doi.org/10.3390/electronics15143174 - 19 Jul 2026

Abstract

In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address [...] Read more.

In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address the limitations of existing methods in short-term dynamic characterization, complex spatial interaction modeling, and heterogeneous target prediction, this paper proposes a Spatio-Temporal Graph Attention Network (TA-STGAT). The proposed model constructs multi-dimensional RSU-level state sequences from simulated trajectories generated on a real-world road network and separately forecasts vehicle flow within RSU coverage areas and the associated traffic-state-derived workload representation under a unified spatio-temporal modeling framework. By integrating gated dilated temporal convolutions with a topology-constrained multi-head graph attention mechanism, the model captures multi-scale temporal dependencies and nonlinear spatial correlations. Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting. Full article

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

28 pages, 25769 KB

Open AccessArticle

Real-Time Neuroadaptive Control with Tactile Calibration for Physical Human–Robot Interaction

by Ashutosh Prakash, Mohamed A. Hanafy, Jordan Dowdy and Dan O. Popa

Electronics 2026, 15(14), 3173; https://doi.org/10.3390/electronics15143173 - 19 Jul 2026

Abstract

We present a neuroadaptive control framework applied to a tactile sensing interface for real-time, human-guided physical interaction with a robotic arm. The framework employs a dual-loop architecture consisting of an inner-loop neuroadaptive controller that compensates for nonlinear robot dynamics and an outer-loop ARMA–RLS [...] Read more.

We present a neuroadaptive control framework applied to a tactile sensing interface for real-time, human-guided physical interaction with a robotic arm. The framework employs a dual-loop architecture consisting of an inner-loop neuroadaptive controller that compensates for nonlinear robot dynamics and an outer-loop ARMA–RLS tactile mapping that converts tactile sensor voltages into planar end-effector displacement commands. Four piezoresistive tactile sensors mounted on the robot end-effector are calibrated individually using autoregressive moving-average (ARMA) models updated through recursive least squares (RLS). The proposed tactile interface does not estimate an absolute Cartesian force/torque wrench; instead, it learns a user- and sensor-specific voltage-to-motion command mapping for planar guidance. To evaluate robustness to user variability, 28 participants completed the calibration experiments, producing 112 user- and sensor-specific calibration models. The calibration procedure achieved millimeter-level displacement-prediction accuracy, with a mean RMSE of approximately 2.70 mm across participants. After calibration, participants used the tactile interface to guide the robot along a predefined figure-eight trajectory. The average nearest-path tracking error decreased from

11.07 \pm 5.45

mm in the initial trial to

8.41 \pm 3.48

mm in the final trial, indicating improved tactile-guided path following after repeated exposure to the interface. During these experiments, the inner neuroadaptive controller maintained bounded joint-space tracking errors. Overall, the proposed calibration and control framework provides a low-cost physical interface for planar human-guided robot motion without requiring a wrist-mounted force/torque sensor. Full article

(This article belongs to the Special Issue New Trends in Soft Robotics and Mechatronics)

22 pages, 564 KB

Open AccessArticle

Efficient 3D Indoor Visible Light Positioning via Stabilized Regularized Least Squares

by Yang Wang and Xiaona Liu

Electronics 2026, 15(14), 3172; https://doi.org/10.3390/electronics15143172 - 19 Jul 2026

Abstract

We study indoor visible light positioning (VLP) in a fully three-dimensional setting under thermal noise and signal-dependent shot noise. Existing shot-noise-aware least-squares (LS) VLP methods largely rely on a fixed receiver height, so their key simplifications break down when the vertical coordinate is [...] Read more.

We study indoor visible light positioning (VLP) in a fully three-dimensional setting under thermal noise and signal-dependent shot noise. Existing shot-noise-aware least-squares (LS) VLP methods largely rely on a fixed receiver height, so their key simplifications break down when the vertical coordinate is unknown. We derive a 3D likelihood-inspired surrogate, lift the nonlinear geometry into a weighted LS form, and add a geometry-aware regularizer that restores approximate consistency among redundant lifted variables. To make the iterative solver reliable in sparse layouts, we introduce room-feasible projection, consistent pilot reconstruction, clipped frozen weights, and damped objective-accepted updates. Each inner step remains a closed-form 4 × 4 linear solve. In deterministic synthetic simulations of an 8 × 8 × 3 m room with layouts containing 4, 9, and 16 light-emitting diodes (LEDs), high- and low-power regimes, and 400 test samples per scenario, the stabilized solver removes the catastrophic sparse-layout failures observed for the tested LS-family baselines and retains a 14×–23× runtime advantage over the nonlinear baseline. The nonlinear baseline often attains lower mean error in sparse and moderate layouts; the contribution here is the resulting speed–tail-control trade-off within the fixed-orientation line-of-sight model. The resulting estimator is intended for synthetic 3D VLP simulations; hardware validation is outside the present study. Full article

(This article belongs to the Topic Visible Light Communications for Localization, Sensing and IoT)

22 pages, 903 KB

Open AccessArticle

Intelligent UAV Trajectory Design and Task Offloading for UAV-Assisted Edge Computing in Urban Road Scenarios

by Xiong Wu, Wenxiang Chen, Pengfei Du, Junyu Guo, Xia Liu and Keqiu Chen

Electronics 2026, 15(14), 3171; https://doi.org/10.3390/electronics15143171 - 19 Jul 2026

Abstract

With the rapid proliferation of internet of vehicles applications, vehicle users in urban road scenarios face ever-increasing demands for low-latency and energy-efficient processing of computation-intensive tasks. The traditional fixed terrestrial infrastructure offers limited coverage in complex urban environments, making it difficult to satisfy [...] Read more.

With the rapid proliferation of internet of vehicles applications, vehicle users in urban road scenarios face ever-increasing demands for low-latency and energy-efficient processing of computation-intensive tasks. The traditional fixed terrestrial infrastructure offers limited coverage in complex urban environments, making it difficult to satisfy the differentiated quality of service requirements of large-scale vehicle populations. To fully exploit the advantages of unmanned aerial vehicles (UAVs) in terms of flexible deployment and on-demand service provisioning, we propose a UAV-assisted mobile edge computing architecture tailored for urban road scenarios. By modeling realistic urban road terrain with varying elevations, we construct a two-tier cooperative network consisting of multiple rotary wing UAVs and ground vehicles. Aiming at maximizing the total system energy consumption, we formulate a mixed integer nonlinear programming problem that minimizes total system energy consumption through joint optimization of UAV flight trajectories and vehicle task offloading decisions while comprehensively accounting for task latency constraints, UAV flight velocity constraints, and the impact of three-dimensional terrain on air-to-ground channels. Considering the high-dimensional, non-convex, mixed integer, and strongly coupled nature of the problem, we design a genetic algorithm (GA)-based UAV trajectory design and offload allocation algorithm. The proposed approach encodes UAV trajectories as real-valued vectors and offloading decisions as binary vectors, employs a penalty function method to handle constraints, and achieves efficient global search through tournament selection, single-point crossover, and Gaussian mutation operators. Simulation results verify that the proposed algorithm converges reliably to feasible solutions under varying task data sizes and vehicle densities and achieves up to 20.6% energy savings compared to the benchmark schemes. The experimental results validate the necessity and effectiveness of jointly optimizing UAV trajectory and task offloading in urban road scenarios. Full article

(This article belongs to the Special Issue Recent Developments and Emerging Trends of UAV Networks)

22 pages, 1500 KB

Open AccessArticle

Evaluation of Multi-Model Architecture Against Single-Model PPO in the MiniGrid Environment

by Igor Ciganović, Aleksandar Pluškoski, Miloš Jovanović and Jelena Vasiljević

Electronics 2026, 15(14), 3170; https://doi.org/10.3390/electronics15143170 - 19 Jul 2026

Abstract

Reward sparsity is a considerable problem in reinforcement learning when solving complex problems. In an environment where the reward is given only after successfully solving a problem in multiple steps, the signals for optimization can be too weak, preventing the agent from learning. [...] Read more.

Reward sparsity is a considerable problem in reinforcement learning when solving complex problems. In an environment where the reward is given only after successfully solving a problem in multiple steps, the signals for optimization can be too weak, preventing the agent from learning. However, this can be solved by breaking the problem into an array of simpler tasks that the agent can learn to perform. This paper presents a possible solution that uses a mixture-of-experts architecture. Separate models are used for planning, routing expert outputs, and solving singular sub-tasks. The solution was implemented in the MiniGrid environment, which allows for the design of arbitrarily complex problems. The results presented in this paper show that a multi-model agent can solve more complex problems comprising multiple steps than a single reinforcement learning agent. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

28 pages, 3142 KB

Open AccessArticle

Intelligent Edge-Cloud Data Management with a Predictive Smart Offloading Proxy for 5G Internet of Vehicles

by Ray-I Chang, Ting-Wei Hsu, Jui-En Hsieh, Chih Yang and Yen-Ting Chen

Electronics 2026, 15(14), 3169; https://doi.org/10.3390/electronics15143169 - 19 Jul 2026

Abstract

Connected vehicles generate video, sensor, and bulk data that must be uploaded, cached, and forwarded across edge and cloud resources under short contact durations and congested backhaul. This paper studies selected data-management functions of a Smart Offloading Proxy (SOP) for 5G Internet of [...] Read more.

Connected vehicles generate video, sensor, and bulk data that must be uploaded, cached, and forwarded across edge and cloud resources under short contact durations and congested backhaul. This paper studies selected data-management functions of a Smart Offloading Proxy (SOP) for 5G Internet of Vehicles (IoV): deadline-constrained scheduling of uploads already accepted at the edge proxy, and a radio-quality allocation signal. Control-plane functions are described but not evaluated; no end-to-end architecture validation is claimed. For proxy-side forwarding, six bandwidth-scheduling policies are formalized and evaluated in NS-3 against a first-come-first-served serve-one baseline. With bursty arrivals, a 60 Mbps bottleneck, and a 30 s dwell deadline, the shortest-remaining-k equal-allocation policy (SRK-EQ) is the strongest of the six scheduling policies, completing 92.5 ± 4.6% over 20 seeds versus 75.2 ± 12.0% for the all-jobs baseline; the serve-one baseline attains higher completion (98.0 ± 2.4%) for homogeneous 10 MB jobs. Under a heterogeneous 1/10/50 MB workload, SRK-EQ delivers lower latency (median 3.15 s versus 19.84 s) with overlapping completion estimates and a large-job fairness trade-off. In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated. Full article

(This article belongs to the Special Issue AI-Driven Data Integration and Intelligent Data Management for Smart Digital Systems)

► Show Figures

Figure 1

15 pages, 7353 KB

Open AccessArticle

Hybrid Balanced Power Amplifier at 5.8 GHz Using GaN HEMTS and Sierpinski Space-Filling Couplers Design Approach

by Iago Alvarez-Ramos, Ainhoa Morales-Fernandez, Maria Marante-Boado, Monica Fernandez-Barciela and Fernando Martin-Rodriguez

Electronics 2026, 15(14), 3168; https://doi.org/10.3390/electronics15143168 - 18 Jul 2026

Abstract

A hybrid balanced power amplifier operating in C-band at 5.8 GHz has been designed for UAV communications using packaged GaN HEMTs and microstrip technology. To reduce the overall circuit footprint, Sierpinski space-filling curves are employed in the design of the hybrid quadrature couplers, [...] Read more.

A hybrid balanced power amplifier operating in C-band at 5.8 GHz has been designed for UAV communications using packaged GaN HEMTs and microstrip technology. To reduce the overall circuit footprint, Sierpinski space-filling curves are employed in the design of the hybrid quadrature couplers, allowing almost 40% miniaturization without degrading performance. The manufactured balanced amplifier prototype exhibits, at the carrier frequency and at 3 dB gain compression, a measured output RF power of 38.9 dBm (≈8 W) and a drain efficiency of 53%. Full article

(This article belongs to the Special Issue Advances in GaN Semiconductors Technology: Materials & Devices for the Next Generation of High-Efficiency Power Electronics)

► Show Figures

Figure 1

29 pages, 2044 KB

Open AccessArticle

A New Multidimensional Data Anonymization Algorithm for Privacy-Preserving Data Publishing

by Burak Cem Kara, Can Eyüpoğlu and Oktay Karakuş

Electronics 2026, 15(14), 3167; https://doi.org/10.3390/electronics15143167 - 18 Jul 2026

Abstract

Developing a privacy-preserving data publishing algorithm that prevents individuals’ identity information from being disclosed without ignoring the utility of the data is still an important goal to be achieved. Finding the optimal balance between data utility and data privacy is an NP-hard problem. [...] Read more.

Developing a privacy-preserving data publishing algorithm that prevents individuals’ identity information from being disclosed without ignoring the utility of the data is still an important goal to be achieved. Finding the optimal balance between data utility and data privacy is an NP-hard problem. In this study, a new k-anonymization-based data anonymization algorithm is proposed. The proposed algorithm partitions the data space with a new efficient partitioning strategy based on the k-dimensional tree (KD-tree), resolves the boundary problem while maintaining the trade-off balance, and introduces a new mechanism to address the problems caused by outliers. Moreover, it can be applied to both numerical and categorical data. The experimental results indicate that the proposed algorithm achieves competitive or improved performance compared with the baseline algorithms across seven commonly used evaluation metrics. Overall, the findings suggest that the proposed framework can improve the utility–privacy trade-off in multidimensional k-anonymization. Full article

(This article belongs to the Section Computer Science & Engineering)

30 pages, 1991 KB

Open AccessArticle

Efficient Data Construction Method for Electromagnetic Fault Diagnosis of Radar Timing Control Boards

by Pengfei Liu, Ziyan Wang, Hui Wang, Bo Jiang and Wenxian Yu

Electronics 2026, 15(14), 3166; https://doi.org/10.3390/electronics15143166 - 18 Jul 2026

Abstract

This work targets four core bottlenecks restricting electromagnetic susceptibility (EMS) fault diagnosis of digital radar boards, including cold-start modeling caused by scarce fault samples, low-efficiency scanning prone to omitting narrow sensitive frequencies, insufficient feature representation of electromagnetic signals, and severe overfitting induced by [...] Read more.

This work targets four core bottlenecks restricting electromagnetic susceptibility (EMS) fault diagnosis of digital radar boards, including cold-start modeling caused by scarce fault samples, low-efficiency scanning prone to omitting narrow sensitive frequencies, insufficient feature representation of electromagnetic signals, and severe overfitting induced by imbalanced data. An integrated EMS dataset construction framework is accordingly proposed for radar timing control boards based on closed-loop electromagnetic interference (EMI)injection and multidimensional key signal point (KSP) monitoring. The framework adopts an adaptive coarse-to-fine scanning scheme to rapidly lock sensitive bands and accurately resolve fault boundaries. It further incorporates the hybrid synthetic minority oversampling technique and random undersampling (SMOTE-RU) strategy and time–frequency feature filtering to balance sample distribution and remove redundant information, yielding standardized high-quality EMS feature datasets. We validate dataset quality on a suite of classic machine learning and deep learning models. Test results show that all baseline networks attain classification accuracy above 85.1%, while the convolutional block attention module (CBAM) attention-augmented model delivers the highest accuracy of 95.0%. Cross-board tests on dissimilar radar boards show that fine-tuning with 10% target samples drastically raises cross-domain accuracy, and moderate interference yields the best generalization above 91.2%. Incurring minimal retraining costs, this lightweight scheme achieves strong cross-hardware generalization to build EMS databases for radar timing boards and implement multiclass fault diagnosis, effectively solving the cold-start problem for new hardware with insufficient historical fault data. Full article

(This article belongs to the Special Issue AI-Enhanced Electromagnetic Sensing and Inverse Imaging)

30 pages, 12512 KB

Open AccessArticle

Concepts and Approaches for Gamified Speech Therapy Applications in the Slovak Language

by Juraj Kováč, Stanislav Ondáš, Ján Staš, Matúš Pleva and Eva Kiktová

Electronics 2026, 15(14), 3165; https://doi.org/10.3390/electronics15143165 - 18 Jul 2026

Abstract

Traditional speech therapy for children often faces challenges such as fear, a lack of patient motivation, unattractive supportive tools, and a critical shortage of specialized technological tools for under-resourced languages like Slovak. This paper presents a novel conceptual digital speech therapy ecosystem and [...] Read more.

Traditional speech therapy for children often faces challenges such as fear, a lack of patient motivation, unattractive supportive tools, and a critical shortage of specialized technological tools for under-resourced languages like Slovak. This paper presents a novel conceptual digital speech therapy ecosystem and core technological approaches developed within the 3PoCube project. To address the engagement gap, the proposed framework leverages gamification across five interconnected pillars which are integrating interactive, game-like environments for home practice with asynchronous offline telemedicine to support the parent–child–clinician dynamic. The technological core utilizes state-of-the-art Self-Supervised Learning (SSL) and Transformer-based Automatic Speech Recognition (ASR) models fine-tuned on a newly compiled Slovak children’s speech corpus, consisting of approximately 5 h of annotated speech from 303 speakers. In our experiments, the best-performing Wav2Vec 2.0 MMS architecture achieved a Word Error Rate (WER) of 15.10%, while the adapted Whisper Turbo achieved 16.33% on spontaneous pediatric speech. Several functional prototypes implementing these models are presented, including Neslovkáčik (a telemedicine portal), Hláskové superhrdinstvo (an articulation app), and LogoHra (a highly customizable virtual board game platform). Preliminary formative feedback from parents and speech-language pathologists indicates perceived acceptability during early prototype testing and suggests the potential to support engagement with the proposed solutions. However, while this study establishes a technical and conceptual foundation for home rehabilitation, rigorous clinical validation is required before definitive therapeutic efficacy can be claimed. Full article

(This article belongs to the Special Issue Advances in Speech Recognition and Speech Processing Technology)

► Show Figures

Figure 1

17 pages, 3726 KB

Open AccessArticle

Research on the Optimization of Equipotential Entry Path and Station Position for High-Altitude Live Working Personnel Considering the Influence of Insulator String Configuration

by Yong Peng, Yadi Zhang, Zeyu Lei, Zhonghua Qiu, Jie Zhang and Jiaxin Yuan

Electronics 2026, 15(14), 3164; https://doi.org/10.3390/electronics15143164 - 18 Jul 2026

Abstract

To address the reliance on empirical knowledge and the lack of a quantitative basis for worker positioning in high-altitude UHV live-line work, a refined power plant model consisting of workers, transmission towers, insulator strings, and conductors has been established. Based on altitude-corrected breakdown [...] Read more.

To address the reliance on empirical knowledge and the lack of a quantitative basis for worker positioning in high-altitude UHV live-line work, a refined power plant model consisting of workers, transmission towers, insulator strings, and conductors has been established. Based on altitude-corrected breakdown field strength and discharge attachment criteria, entry paths and positioning for the cradle and power-driven ascender methods were optimized under IVI- and VVV-type insulator strings. Results show that field concentration is more pronounced in the middle phase. For the cradle method, a 30° approach angle is optimal, reducing body surface field strength by 9.4–18.6%, with discharge points at the toes; a sitting posture with slightly raised legs and toes pointing toward the conductor is recommended. For the power-driven ascender method, vertical entry from the side outperforms entry from below, reducing the far-tower side-phase field strength by 22–24%, with discharge points at the head; an upright, sideways posture with the face turned away from the conductor is recommended. This study provides a quantitative basis for high-altitude UHV live-line work. Full article

► Show Figures

Figure 1

20 pages, 1170 KB

Open AccessArticle

A Lattice-Based Underwater Aggregate Signcryption Scheme

by Zhehui Zhang and Ming Xu

Electronics 2026, 15(14), 3163; https://doi.org/10.3390/electronics15143163 - 18 Jul 2026

Abstract

For underwater acoustic sensor networks (UWSNs), we propose a lattice-based aggregate signcryption scheme (UASC) to achieve many to one secure and efficient communication in UWSNs. Firstly, UASC constructs the pseudo identity of the node, performs lattice Gaussian sampling on the pseudo identity to [...] Read more.

For underwater acoustic sensor networks (UWSNs), we propose a lattice-based aggregate signcryption scheme (UASC) to achieve many to one secure and efficient communication in UWSNs. Firstly, UASC constructs the pseudo identity of the node, performs lattice Gaussian sampling on the pseudo identity to generate a partial key, and then combines the underwater environmental noise collected by the node to generate a complete private key, to avoid the key generation center (KGC) leakage problem. Furthermore, UASC reuses the mathematical structures of encryption and signature on the lattice, designing a signcryption framework to achieve identity authentication and data confidentiality protection. By using the relay capability of the surface sink node, UASC combines with the ring learning with errors (RLWE) encryption and the rejection sampling to perform batch signature verification on the aggregate ciphertext set, which improves the overall response efficiency of the system. In addition, to resist tracking attacks, a dynamic update mechanism of pseudo identity and key is designed in UASC. Security analysis and experimental results show that UASC reduces overhead while satisfying relevant security requirements. Full article

38 pages, 13994 KB

Open AccessArticle

Design and Deployment of an Open-Source Multi-Tenant IoT Cloud Architecture for Renewable-Energy Living Labs in Rwanda

by Eraste Rukundo, Mirco Mongilli, Viviane Ishimwe, Guido Matrella and Paolo Ciampolini

Electronics 2026, 15(14), 3162; https://doi.org/10.3390/electronics15143162 - 18 Jul 2026

Abstract

IoT cloud platforms support the monitoring and management of distributed renewable-energy systems, but sustaining them can be challenging in academic and community initiatives with limited resources. Recurring cloud-service fees, dependence on proprietary platforms, and limited local control may become major obstacles, particularly when [...] Read more.

IoT cloud platforms support the monitoring and management of distributed renewable-energy systems, but sustaining them can be challenging in academic and community initiatives with limited resources. Recurring cloud-service fees, dependence on proprietary platforms, and limited local control may become major obstacles, particularly when long-term service operation must be combined with technical capacity-building within local institutions and surrounding communities. This paper presents an open-source, multi-tenant IoT cloud architecture developed within the GREATER Erasmus+ framework and deployed in renewable-energy Living Labs in Rwanda. The architecture is built using widely adopted open-source tools, including Docker, Node-RED, MySQL, Nginx, MQTT, and HTTPS APIs. The main novelty is an open-source middleware layer that transforms standard components into a shared, tenant-aware platform by coordinating authentication, role-based access, tenant-aware routing, controlled database access, separate Node-RED workspaces, and dashboard visibility for different Living Labs and user roles. The platform has been deployed in real settings, supporting photovoltaic monitoring, solar-powered irrigation, community energy services, domestic energy monitoring, and educational activities. The platform is evaluated in terms of cost, resource usage, communication delay, reliability mechanisms, and access-control behavior. The results show that heterogeneous IoT services can be hosted on modest local infrastructure while maintaining logical separation between Living Lab data and services. By combining open-source technologies, multi-tenant management, and field deployment, the proposed architecture offers a practical model for higher-education institutions and community-oriented renewable-energy initiatives in resource-constrained environments. Full article

(This article belongs to the Special Issue Enhancing Security, Reliability and Service Quality for IoT-Based Systems)

► Show Figures

Figure 1

12 pages, 3492 KB

Open AccessCommunication

A Metasurface Filtering Antenna with Rectangular Patch Structures of Different Sizes

by Jun Li, Yu-Feng Tan and Dong-Sheng La

Electronics 2026, 15(14), 3161; https://doi.org/10.3390/electronics15143161 - 18 Jul 2026

Abstract

This paper presents a metasurface filtering antenna based on unequal rectangular patch elements. The design is implemented with only two dielectric layers: the upper PCB carries the metasurface radiator, and the lower PCB supports a Y-shaped microstrip feeding network. A radiation null near [...] Read more.

This paper presents a metasurface filtering antenna based on unequal rectangular patch elements. The design is implemented with only two dielectric layers: the upper PCB carries the metasurface radiator, and the lower PCB supports a Y-shaped microstrip feeding network. A radiation null near 3.3 GHz is obtained by dividing the four corner units of the metasurface into 2 × 2 subarrays, which reshapes the current paths around the central aperture and causes destructive spatial radiation. For the upper stopband, a second null appears at 3.84 GHz because the fields coupled from the opposite-phase currents on the Y-shaped feed branches cancel in the far field. Measurements indicate that the antenna provides a 10 dB impedance bandwidth of 3.43–3.60 GHz, or 9.85% in fractional bandwidth. Within the passband, the measured average realized gain reaches 7.51 dBi. The lower and upper stopbands exhibit radiation suppression levels of 15.61 dB and 14.08 dB, respectively. The boresight cross-polarized field is also more than 20 dB below the co-polarized field, confirming satisfactory radiation quality. These results make the proposed antenna suitable for compact 5G front-end integration. The proposed design provides a balanced combination of low profile, compact aperture, measured gain, and two band-edge radiation nulls. Full article

► Show Figures

Figure 1

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