Electronics

33 pages, 3576 KB

Open AccessArticle

Small-Signal Modeling, Comparative Analysis, and Gain-Scheduled Control of DC–DC Converters in Photovoltaic Applications

by Vipinkumar Shriram Meshram, Fabio Corti, Gabriele Maria Lozito, Luigi Costanzo, Alberto Reatti and Massimo Vitelli

Electronics 2025, 14(21), 4308; https://doi.org/10.3390/electronics14214308 (registering DOI) - 31 Oct 2025

Abstract

This paper presents an innovative approach to the modeling and dynamic analysis of DC–DC converters in photovoltaic applications. Departing from traditional studies that focus on the transfer function from duty cycle to output voltage, this work investigates the duty cycle to input voltage [...] Read more.

This paper presents an innovative approach to the modeling and dynamic analysis of DC–DC converters in photovoltaic applications. Departing from traditional studies that focus on the transfer function from duty cycle to output voltage, this work investigates the duty cycle to input voltage transfer function, which is critical for accurate dynamic representation of photovoltaic systems. A notable contribution of this study is the integration of the PV panel behavior in the small-signal representation, considering a model-derived differential resistance for various operating points. This technique enhances the model’s accuracy across different operating regions. The paper also validates the effectiveness of this linearization method through small-signal analysis. A comprehensive comparison is conducted among several non-isolated converter topologies such as Boost, Buck–Boost, Ćuk, and SEPIC under both open-loop and closed-loop conditions. To ensure fairness, all converters are designed using a consistent set of constraints, and controllers are tuned to maintain similar phase margins and crossover frequencies across topologies. In addition, a gain-scheduling control strategy is implemented for the Boost converter, where the PI gains are dynamically adapted as a function of the PV operating point. This approach demonstrates superior closed-loop performance compared to a fixed controller tuned only at the maximum power point, further highlighting the benefits of the proposed modeling and control framework. This systematic study therefore provides an objective evaluation of dynamic performance and offers valuable insights into optimal converter architectures and advanced control strategies for photovoltaic systems. Full article

(This article belongs to the Special Issue New Horizons and Recent Advances of Power Electronics)

28 pages, 2145 KB

Open AccessArticle

Port Microgrid Capacity Planning Under Tightening Carbon Constraints: A Bi-Level Cost Optimization Framework

by Junyang Ma and Yin Zhang

Electronics 2025, 14(21), 4307; https://doi.org/10.3390/electronics14214307 (registering DOI) - 31 Oct 2025

Abstract

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term [...] Read more.

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO₂ emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication. Full article

14 pages, 416 KB

Open AccessArticle

A QMIX-Based Multi-Agent Reinforcement Learning Approach for Crowdsourced Order Assignment in Fresh Food Retailing

by Jingming Hu and Chong Wang

Electronics 2025, 14(21), 4306; https://doi.org/10.3390/electronics14214306 (registering DOI) - 31 Oct 2025

Abstract

Crowdsourced delivery plays a key role in fresh food retailing, where tight time limits and perishability require fast, reliable fulfillment. However, real-time order–courier assignment is challenging because orders arrive in bursts, couriers’ locations and availability change, capacities are limited, and many decisions must [...] Read more.

Crowdsourced delivery plays a key role in fresh food retailing, where tight time limits and perishability require fast, reliable fulfillment. However, real-time order–courier assignment is challenging because orders arrive in bursts, couriers’ locations and availability change, capacities are limited, and many decisions must be made simultaneously. We propose Attn-QMIX, a novel attention-augmented multi-agent reinforcement learning framework that models each order as an agent and learns coordinated matching strategies through centralized training with decentralized execution. The framework develops a new capacity-aware multi-head attention mechanism that captures complex order–courier interactions and dynamically prevents courier overload and integrates it with a QMIX-based mixing network equipped with hypernetworks to enable effective credit assignment and global coordination. Extensive experiments on a real-world road network show that Attn-QMIX outperforms five representative methods. Compared with a novel cooperative ant colony optimization method, it reduces total cost by up to 2.30% while being up to 3403 times faster in computation. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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21 pages, 2010 KB

Open AccessArticle

PV-Scope Test System: Photovoltaic Module Characterization with Maximum Power, Efficiency, and Environmental Sensing

by Christi K. Madsen and Bitian Jiang

Electronics 2025, 14(21), 4305; https://doi.org/10.3390/electronics14214305 (registering DOI) - 31 Oct 2025

Abstract

An integrated ESP32-based measurement system called PV-Scope is presented for real-time photovoltaic (PV) module efficiency characterization and small off-grid system testing under field conditions. The system includes pyranometer-calibrated irradiance sensors using a solar simulator, maximum power point tracking, and comprehensive environmental monitoring to [...] Read more.

An integrated ESP32-based measurement system called PV-Scope is presented for real-time photovoltaic (PV) module efficiency characterization and small off-grid system testing under field conditions. The system includes pyranometer-calibrated irradiance sensors using a solar simulator, maximum power point tracking, and comprehensive environmental monitoring to enable accurate performance assessment of PV modules across diverse technologies, manufacturers and installation conditions. Unlike standard test condition (STC) measurements at cell temperatures of 25 °C, this system captures the interactions between efficiency and environmental variables that significantly impact real-world efficiency. In particular, measurement of temperature-dependent efficiency under local conditions and validation of temperature-dependent models for extending the results to other environmental conditions are enabled with cell temperature monitoring in addition to ambient temperature, humidity, and wind speed. PV-Scope is designed for integrated sensing versatility, portable outdoor testing, and order-of-magnitude cost savings compared to commercial equipment to meet measurement needs across research, education, and practical PV innovation, including bifacial module testing, assessment of cooling techniques, tandem and multi-junction testing, and agrivoltaics. Full article

25 pages, 1618 KB

Open AccessArticle

An Emotional AI Chatbot Using an Ontology and a Novel Audiovisual Emotion Transformer for Improving Nonverbal Communication

by Yun Wang, Liege Cheung, Patrick Ma, Herbert Lee and Adela S.M. Lau

Electronics 2025, 14(21), 4304; https://doi.org/10.3390/electronics14214304 (registering DOI) - 31 Oct 2025

Abstract

One of the key limitations of AI chatbots is the lack of human-like nonverbal communication. Although there are many research studies on video or audio emotion recognition for detecting human emotions, there is no research that combines video, audio, and ontology methods to [...] Read more.

One of the key limitations of AI chatbots is the lack of human-like nonverbal communication. Although there are many research studies on video or audio emotion recognition for detecting human emotions, there is no research that combines video, audio, and ontology methods to develop an AI chatbot with human-like communication. Therefore, this research aims to develop an audio-video emotion recognition model and an emotion-ontology-based chatbot engine to improve human-like communication with emotion detection. This research proposed a novel model of cluster-based audiovisual emotion recognition for improving emotion detection with both video and audio signals and compared it with existing methods using video or audio signals only. Twenty-two audio features, the Mel spectrogram, and facial action units were extracted, and the last two were fed into a cluster-based independent transformer to learn long-term temporal dependencies. Our model was validated on three public audiovisual datasets: RAVDESS, SAVEE, and RML. The results demonstrated that the accuracy scores of the clustered transformer model for RAVDESS, SAVEE, and RML were 86.46%, 92.71%, and 91.67%, respectively, outperforming the existing best model with accuracy scores of 86.3%, 75%, and 60.2%, respectively. An emotion-ontology-based chatbot engine was implemented to make inquiry responses based on the detected emotion. A case study of the HKU Campusland metaverse was used as proof of concept of the emotional AI chatbot for nonverbal communication. Full article

(This article belongs to the Special Issue Real-Time Audio, Video and Image Processing: Latest Advances and Prospects)

22 pages, 2947 KB

Open AccessArticle

Explaining Grid Strength Through Data: Key Factors from a Southwest China Power Grid Case Study

by Liang Lu, Hong Zhou, Shaorong Cai, Yuxuan Tao and Yuxiao Yang

Electronics 2025, 14(21), 4303; https://doi.org/10.3390/electronics14214303 (registering DOI) - 31 Oct 2025

Abstract

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is [...] Read more.

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is developed using the AHP-CRITIC method to fuse structural and fault withstand metrics. Then, to identify the factors influencing this index, SHapley Additive exPlanations (SHAP) is employed, accelerated by a high-fidelity Gaussian Process Regression (GPR) surrogate model that overcomes the computational burden of large-scale simulations. This GPR-SHAP approach provides both global parameter rankings and local, scenario-specific explanations, overcoming the limitations of conventional sensitivity analysis. Validated on a detailed model of the Southwest Power Grid in China, the framework successfully quantifies grid strength and pinpoints key vulnerabilities. Verification through a typical scenario demonstrates that implementing coordinated increases in both generation and load (each by 1000 MW) in the Chengdu area, as guided by local SHAP explanations, significantly improves the grid strength index from 33.73 to 47.61. It provides operators with a dependable tool to transition from experience-based practices to targeted, proactive stability management. Full article

(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated, 2nd Edition)

20 pages, 6222 KB

Open AccessArticle

Electric-Field and Magnetic-Field Decoupled Wireless Power and Full-Duplex Signal Transfer Technology for Pre-Embedded Sensors

by Xiaolong Wang, Xiaozhou Wei and Laiqiang Jia

Electronics 2025, 14(21), 4302; https://doi.org/10.3390/electronics14214302 (registering DOI) - 31 Oct 2025

Abstract

Pre-embedded sensors for concrete structure monitoring face bottlenecks in power supply and data transmission. Existing power supply solutions such as photovoltaic systems and batteries suffer from drawbacks including energy randomness and structural damage to concrete caused by their installation methods. Additionally, commercial wireless [...] Read more.

Pre-embedded sensors for concrete structure monitoring face bottlenecks in power supply and data transmission. Existing power supply solutions such as photovoltaic systems and batteries suffer from drawbacks including energy randomness and structural damage to concrete caused by their installation methods. Additionally, commercial wireless communication signals exhibit issues like strong attenuation and poor security during propagation. This paper proposes a hybrid electromagnetic field decoupled parallel transmission technology for power and signals. It utilizes the inherent decoupling characteristic of electric and magnetic fields within the near-field range to construct independent power/signal transfer channels, and achieves independent full-duplex transmission of uplink/downlink data via orthogonal coupling plates. Mathematical models for the power and signal channels are established, and finite element simulations are conducted. A parameter design method for the power compensation network and signal filtering circuit is also proposed. An experimental setup is built, with a coupler outer dimension of 200 mm × 200 mm, a coupling distance of 10 mm, and a thickness of 16 mm for both the transmitting and receiving sides. Experimental results show that the system achieves power transmission with a power of 100 W and an efficiency of 82%, while simultaneously realizing full-duplex communication with a bidirectional rate of 9600 bit/s. Moreover, no bit errors occur within 300,000 characters of bidirectional data. Full article

(This article belongs to the Special Issue Next-Generation WPT: Emerging Principles, Technologies, and Applications)

23 pages, 6414 KB

Open AccessArticle

WeDIGAR: A Light-Weighted Webshell Detection Framework for Satellite and UAV Networks

by Shun Fu, Hao Li, Panpan Zhu, Jian Tong, Jinye Yang and Ji Xu

Electronics 2025, 14(21), 4301; https://doi.org/10.3390/electronics14214301 (registering DOI) - 31 Oct 2025

Abstract

In satellite and Unmanned Aerial Vehicle (UAV) networks, detecting webshells presents unique challenges, particularly on ground station edge nodes. Large, resource-intensive detection models are not feasible as nodes have insufficient computing power and limited time for analysis. This paper introduces a novel approach [...] Read more.

In satellite and Unmanned Aerial Vehicle (UAV) networks, detecting webshells presents unique challenges, particularly on ground station edge nodes. Large, resource-intensive detection models are not feasible as nodes have insufficient computing power and limited time for analysis. This paper introduces a novel approach for webshell detection tailored for these environments. Our method first extracts structural and semantic Information Granules (IGs) from the HTTP response bodies sent from the remote systems. Next, we construct a causal graph to identify and remove irrelevant IGs that are not linked to the webshell label. Finally, a random forest classifier is applied to the remaining, relevant IGs. This lightweight component has been empirically validated in both laboratory experiments and a simulated industrial application scenario. The results show that our method achieved an impressive accuracy rate exceeding 99% and a response time of less than 10 milliseconds for each request, significantly outperforming legacy systems based on graph convolutional networks. Full article

(This article belongs to the Special Issue Advances in Satellite/UAV Communications)

45 pages, 6602 KB

Open AccessArticle

Novel Design and Experimental Validation of a Technique for Suppressing Distortion Originating from Various Sources in Multiantenna Full-Duplex Systems

by Keng-Hwa Liu, Juinn-Horng Deng and Min-Siou Yang

Electronics 2025, 14(21), 4300; https://doi.org/10.3390/electronics14214300 (registering DOI) - 31 Oct 2025

Abstract

Complex distortion cancellation methods are often used at the radio frequency (RF) front end of multiantenna full-duplex transceivers to mitigate signal distortion; however, these methods have high computational complexity and limited practicality. To address these problems, the present study explored the complexities associated [...] Read more.

Complex distortion cancellation methods are often used at the radio frequency (RF) front end of multiantenna full-duplex transceivers to mitigate signal distortion; however, these methods have high computational complexity and limited practicality. To address these problems, the present study explored the complexities associated with such transceivers to develop a practical multistep approach for suppressing distortions arising from in-phase and quadrature (I/Q) imbalance, nonlinear power amplifier (PA) responses, and multipath self-interference caused by simultaneous transmissions on the same frequency. In this approach, the I/Q imbalance is estimated and then compensated for, following which nonlinear PA distortion is estimated and pre-compensated for. Subsequently, an auxiliary RF transmitter is combined with linearly regenerating self-interference signals to achieve full-duplex self-interference cancellation. The proposed method was implemented on a software-defined radio platform, with the distortion factor calibration specifically optimized for multiantenna full-duplex transceivers. The experimental results indicate that the image signal caused by I/Q imbalance can be suppressed by up to 60 dB through iterative computation. By combining IQI and DPD preprocessing, the nonlinear distortion spectrum can be reduced by 25 dB. Furthermore, integrating IQI, DPD, and self-interference preprocessing achieves up to 180 dB suppression of self-interference signals. Experimental results also demonstrate that the proposed method achieves approximately 20 dB suppression of self-interference. Thus, the method has high potential for enhancing the performance of multiantenna RF full-duplex systems. Full article

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

13 pages, 10580 KB

Open AccessArticle

A Wide-Input-Range LDO with High Output Accuracy Based on Digital Trimming Technique

by Jian Ren, Hongchun Wang, Meng Li, Bin Liu, Jianshu Xiao and Wei Zhao

Electronics 2025, 14(21), 4299; https://doi.org/10.3390/electronics14214299 (registering DOI) - 31 Oct 2025

Abstract

Temperature is a crucial indicator in monitoring industrial operations. Two-wire temperature transmitters, known for their precise measurements, are extensively used in sectors like crude oil extraction, refining, and fine chemicals. These transmitters can handle a maximum input voltage of 36 V and output [...] Read more.

Temperature is a crucial indicator in monitoring industrial operations. Two-wire temperature transmitters, known for their precise measurements, are extensively used in sectors like crude oil extraction, refining, and fine chemicals. These transmitters can handle a maximum input voltage of 36 V and output a current signal up to 20 mA, enhancing resistance to electromagnetic interference and line noise while improving system compatibility and safety. In contrast, traditional low-dropout linear regulators (LDOs) typically have an input voltage below 6 V and suffer from limitations such as low power supply rejection ratio (PSRR), inadequate current driving capability, and significant temperature drift. This paper proposes a wide-input-range LDO with enhanced output accuracy and digital trimming, designed using the 180 nm BCD process. It incorporates dynamic mismatch compensation, digital trimming, and a strong-drive buffer, achieving a broad input voltage range and high PSRR with minimal temperature drift. The input voltage spans 6 V to 60 V, the output voltage is 1.8 V, and the PSRR reaches 124.5 dB. Across a temperature range of −40 °C to 130 °C, the maximum output voltage error is only 0.3%. This makes it highly suitable for high-precision circuit power supplies in industrial process control. Full article

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

16 pages, 416 KB

Open AccessArticle

From Claims to Stance: Zero-Shot Detection with Pragmatic-Aware Multi-Agent Reasoning

by Zhiyu Xie, Fuqiang Niu, Genan Dai and Bowen Zhang

Electronics 2025, 14(21), 4298; https://doi.org/10.3390/electronics14214298 (registering DOI) - 31 Oct 2025

Abstract

Stance detection aims to identify whether a text expresses a favorable, opposing, or neutral attitude toward a given target and has become increasingly important for analyzing public discourse on social media. Existing approaches, ranging from supervised neural models to prompt-based large language models [...] Read more.

Stance detection aims to identify whether a text expresses a favorable, opposing, or neutral attitude toward a given target and has become increasingly important for analyzing public discourse on social media. Existing approaches, ranging from supervised neural models to prompt-based large language models (LLMs), face two persistent challenges: the scarcity of annotated stance data across diverse targets and the difficulty of generalizing to unseen targets under pragmatic and rhetorical variation. To address these issues, we propose PAMR (Pragmatic-Aware Multi-Agent Reasoning), a zero-shot stance detection framework that decomposes stance inference into structured reasoning steps. PAMR orchestrates three LLM-driven agents—a linguistic parser that extracts pragmatic markers and canonicalizes claims, an NLI-based estimator that produces calibrated stance probabilities through consensus voting, and a counterfactual and view-switching auditor that probes robustness under controlled rewrites. A stability-aware fusion integrates these signals, conservatively abstaining when evidence is uncertain or inconsistent. Experiments on SemEval-2016 and COVID-19-Stance show that PAMR achieves macro-F1 scores of 71.9% and 73.0%, surpassing strong zero-shot baselines (FOLAR and LogiMDF) by +2.0% and +3.1%. Ablation results confirm that pragmatic cues and counterfactual reasoning substantially enhance robustness and interpretability, underscoring the value of explicit reasoning and pragmatic awareness for reliable zero-shot stance detection on social media. Full article

(This article belongs to the Special Issue Advances in Social Bots)

18 pages, 1909 KB

Open AccessArticle

Generalizable Interaction Recognition for Learning from Demonstration Using Wrist and Object Trajectories

by Jagannatha Charjee Pyaraka, Mats Isaksson, John McCormick, Sheila Sutjipto and Fouad Sukkar

Electronics 2025, 14(21), 4297; https://doi.org/10.3390/electronics14214297 (registering DOI) - 31 Oct 2025

Abstract

Learning from Demonstration (LfD) enables robots to acquire manipulation skills by observing human actions. However, existing methods often face challenges such as high computational cost, limited generalizability, and a loss of key interaction details. This study presents a compact representation for interaction recognition [...] Read more.

Learning from Demonstration (LfD) enables robots to acquire manipulation skills by observing human actions. However, existing methods often face challenges such as high computational cost, limited generalizability, and a loss of key interaction details. This study presents a compact representation for interaction recognition in LfD that encodes human–object interactions using 2D wrist trajectories and 3D object poses. A lightweight extraction pipeline combines MediaPipe-based wrist tracking with FoundationPose-based 6-DoF object estimation to obtain these trajectories directly from RGB-D video without specialized sensors or heavy preprocessing. Experiments on the GRAB and FPHA datasets show that the representation effectively captures task-relevant interactions, achieving 94.6% accuracy on GRAB and 96.0% on FPHA with well-calibrated probability predictions. Both Bidirectional Long Short-Term Memory (Bi-LSTM) with attention and Transformer architectures deliver consistent performance, confirming robustness and generalizability. The method achieves sub-second inference, a memory footprint under 1 GB, and reliable operation on both GPU and CPU platforms, enabling deployment on edge devices such as NVIDIA Jetson. By bridging pose-based and object-centric paradigms, this approach offers a compact and efficient foundation for scalable robot learning while preserving essential spatiotemporal dynamics. Full article

(This article belongs to the Section Artificial Intelligence)

15 pages, 957 KB

Open AccessArticle

DMBT Decoupled Multi-Modal Binding Transformer for Multimodal Sentiment Analysis

by Rui Guo, Gu Gong and Fan Jiang

Electronics 2025, 14(21), 4296; https://doi.org/10.3390/electronics14214296 (registering DOI) - 31 Oct 2025

Abstract

The performance of Multimodal Sentiment Analysis (MSA) is commonly hindered by two major bottlenecks: the complexity and redundancy associated with supervised feature disentanglement and the coarse granularity of static fusion mechanisms. To systematically address these challenges, a novel framework, the Decoupled Multi-modal Binding [...] Read more.

The performance of Multimodal Sentiment Analysis (MSA) is commonly hindered by two major bottlenecks: the complexity and redundancy associated with supervised feature disentanglement and the coarse granularity of static fusion mechanisms. To systematically address these challenges, a novel framework, the Decoupled Multi-modal Binding Transformer (DMBT), is proposed. The framework first introduces an Unsupervised Semantic Disentanglement (USD) module, which resolves the issue of complex redundancy by cleanly separating features into modality-common and modality-specific components in a lightweight, parameter-free manner. Subsequently, to tackle the challenge of coarse-grained fusion, a Gated Interaction and Fusion Transformer (GIFT) is constructed as the core engine. The exceptional performance of GIFT is driven by two synergistic components. The first is a Multi-modal Binding Transposed Attention (MBTA) that employs a hybrid convolutional and attention model to concurrently perceive both global context and local fine-grained features, and then a Dynamic Fusion Gate (DFG) that performs final, adaptive decision-making by re-weighting all deeply enhanced representations. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that the proposed DMBT framework surpasses existing state-of-the-art models across all key evaluation metrics. The efficacy of each innovative component is further validated through comprehensive ablation studies. Full article

(This article belongs to the Special Issue Advancements and Challenges in NLP and Linguistic Text-Mining: Techniques, Applications, and Ethical Considerations)

20 pages, 68975 KB

Open AccessArticle

Investigating the Role of Personality in Appearance Preferences for Huggable Communication Interfaces: A User-Centered Study

by Eleuda Nunez, Barbara Sienkiewicz, Valentina Ramirez Millan, Bipin Indurkhya and Kenji Suzuki

Electronics 2025, 14(21), 4295; https://doi.org/10.3390/electronics14214295 (registering DOI) - 31 Oct 2025

Abstract

As alternative remote communication interfaces become increasingly common, ensuring that they seamlessly integrate into daily life has become a pressing design challenge. In this context, what should a huggable communication device look like—should it have arms or a face, or resemble a conventional [...] Read more.

As alternative remote communication interfaces become increasingly common, ensuring that they seamlessly integrate into daily life has become a pressing design challenge. In this context, what should a huggable communication device look like—should it have arms or a face, or resemble a conventional pillow? This study investigates users’ preferences and personalities regarding the appearance of such interfaces for remote emotional interaction. As a case study, we present HugBits, a round, cushion-like device that transmits hugs through visual and tactile feedback. Drawing on the prior literature and a participatory design workshop, we developed seven shape variations and evaluated them through an online survey with 79 Polish participants. The results reveal a consistent preference for less anthropomorphic designs, with users valuing comfort, simplicity, and intuitive affordances such as areas to rest the head or wrap the arms around. Although personality traits did not significantly predict preferences, the findings highlight broader design criteria: huggable communication interfaces, intended to remain visible and available in shared spaces, must balance emotional expressiveness with social acceptability. These insights provide guidelines for designing emotionally engaging, user-centered mediated touch technologies. Full article

(This article belongs to the Special Issue Human-Robot Interaction and Applications: Challenges and Future Perspectives)

40 pages, 5192 KB

Open AccessArticle

Novel Hybrid Analytical-Metaheuristic Optimization for Efficient Photovoltaic Parameter Extraction

by Abdelkader Mekri, Abdellatif Seghiour, Fouad Kaddour, Yassine Boudouaoui, Aissa Chouder and Santiago Silvestre

Electronics 2025, 14(21), 4294; https://doi.org/10.3390/electronics14214294 (registering DOI) - 31 Oct 2025

Abstract

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, [...] Read more.

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction

R^{2} = 0.9882

, power estimation

R^{2} = 0.9730

, and voltage tracking

R^{2} = 0.9613

. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions (

0 - 1014 W / m^{2}

) validates the method’s robustness for practical PV monitoring applications. Full article

(This article belongs to the Special Issue Section Collection Series: Recent Advances in Optoelectronics from Lab to Industry, 2nd Edition)

17 pages, 3911 KB

Open AccessArticle

Crack Diagnosis of Surface-Mount Capacitors Using AI Classification Models with Multi-Parameter Impedance Spectra

by Minkyu Kang, Namgyeong Kim, Hyunwoo Nam, Yong-Seok Lee, Hak-Jun Lee and Tae Yeob Kang

Electronics 2025, 14(21), 4293; https://doi.org/10.3390/electronics14214293 (registering DOI) - 31 Oct 2025

Abstract

Surface-mounted devices (SMDs) are essential components that enable the miniaturization and enhanced performance in electronic products, significantly impacting both circuit performance and reliability. In this study, we propose a non-destructive evaluation method for cracks in SMD capacitors using the artificial intelligence of impedance [...] Read more.

Surface-mounted devices (SMDs) are essential components that enable the miniaturization and enhanced performance in electronic products, significantly impacting both circuit performance and reliability. In this study, we propose a non-destructive evaluation method for cracks in SMD capacitors using the artificial intelligence of impedance spectra. To achieve this, cracks were induced in 132 specimens through incremental displacement using a shear module of a bond tester. At each crack level, frequency-domain spectra were acquired for 14 parameters using an impedance analyzer. Meaningful changes in parameter patterns corresponding to each crack stage were observed, confirming impedance spectroscopy as an effective tool for crack assessment. Through data augmentation, we generated 87,800 datasets representing various crack stages, which were used to train AI models that output crack stages from input impedance spectra. Based on this dataset, six AI models, ConvNeXt, LSTM, Transformer, Logistic Regression, SVM, and Random Forest, were developed to classify crack severity into nine stages. Model-wise, the Random Forest classifier consistently outperformed the other approaches. When trained with single parameters, it achieved its best performance using the dissipation factor, reaching 98.5% accuracy. Furthermore, when the dissipation factor was combined with any of the remaining impedance parameters, the Random Forest model achieved perfect diagnostic performance (100%) across all combinations, highlighting both its robustness and its suitability for multi-parameter learning. These results provide practical guidance for selecting effective parameters and model architectures for impedance spectrum-based crack diagnostics. Full article

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18 pages, 3749 KB

Open AccessArticle

Design of an IoT Mimetic Antenna for Direction Finding

by Razvan D. Tamas

Electronics 2025, 14(21), 4292; https://doi.org/10.3390/electronics14214292 (registering DOI) - 31 Oct 2025

Abstract

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a [...] Read more.

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a Method of Moments (MoM)-based tool to compute resonant frequencies, VSWR, and gain across three frequency bands centered on 433 MHz, 877.5 MHz, and 2.4 GHz. Compared to a brute-force approach requiring 8¹⁴ full-wave simulations, our technique dramatically reduces computing time by performing only 84 simulations, followed by a fine-tuning procedure targeting the antenna segments with the highest contribution to the error figure. The final design provides good gain and VSWR figures over almost all the frequency ranges of interest. Full article

(This article belongs to the Special Issue Antennas for IoT Devices, 2nd Edition)

18 pages, 1537 KB

Open AccessArticle

A Model of Output Power Control Method for Fault Ride-Through in a Single-Phase NPC Inverter-Based Power Conditioning System with IPOS DAB Converter and Battery

by Reo Emoto, Hiroaki Yamada and Tomokazu Mishima

Electronics 2025, 14(21), 4291; https://doi.org/10.3390/electronics14214291 (registering DOI) - 31 Oct 2025

Abstract

Grid-tied inverters must satisfy fault ride-through (FRT) requirements to ensure grid stability during voltage disturbances. However, most existing FRT-related studies have focused on reactive current injection or voltage support functions, with few addressing how the active power reference should be dynamically controlled during [...] Read more.

Grid-tied inverters must satisfy fault ride-through (FRT) requirements to ensure grid stability during voltage disturbances. However, most existing FRT-related studies have focused on reactive current injection or voltage support functions, with few addressing how the active power reference should be dynamically controlled during voltage dips. In addition, few systems enable bidirectional power transfer or provide comprehensive verification under deep voltage dips. To address this issue, this paper proposes an output power control method for FRT in a single-phase neutral-point-clamped (NPC) inverter-based PCS consisting of an input-parallel output-series (IPOS) dual-active-bridge (DAB) converter and a battery. The proposed PCS dynamically reduces the output power reference according to the retained voltage while maintaining the inverter current within the rated limit, thereby ensuring stable operation. Computer simulations were conducted using Altair PSIM to verify the effectiveness of the proposed method. The results confirmed that the PCS satisfied the FRT requirements for all post-fault voltage levels. The injected current returned to its pre-fault value within 20 ms and 90 ms for 20% and 0% voltage dips, respectively, complying with the required recovery times. The proposed control method enhances grid resilience and maintains power quality in single-phase low-voltage distribution systems. Full article

(This article belongs to the Special Issue DC–DC Power Converter Technologies for Energy Storage Integration)

33 pages, 22059 KB

Open AccessReview

Resistive Sensing in Soft Robotic Grippers: A Comprehensive Review of Strain, Tactile, and Ionic Sensors

by Donya Mostaghniyazdi and Shahab Edin Nodehi

Electronics 2025, 14(21), 4290; https://doi.org/10.3390/electronics14214290 (registering DOI) - 31 Oct 2025

Abstract

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique [...] Read more.

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique among transduction processes since it is easy to use, scalable, and compatible with deformable materials. The three main classes of resistive sensors used in soft robotic grippers are systematically examined in this review: ionic sensors, which are emerging multimodal devices that can capture both mechanical and environmental cues; tactile sensors, which detect contact, pressure distribution, and slip; and strain sensors, which monitor deformation and actuation states. Their methods of operation, material systems, fabrication techniques, performance metrics, and integration plans are all compared in the survey. The results show that sensitivity, linearity, durability, and scalability are all trade-offs across sensor categories, with ionic sensors showing promise as a new development for multipurpose soft grippers. There is also a discussion of difficulties, including hysteresis, long-term stability, and signal processing complexity. In order to move resistive sensing from lab prototypes to reliable, practical applications in domains like healthcare, food handling, and human–robot collaboration, the review concludes that developments in hybrid material systems, additive manufacturing, and AI-enhanced signal interpretation will be crucial. Full article

(This article belongs to the Special Issue Advances in Environmental Sensing and Control for Mobile Robotics: Methods, Technologies, and Applications)

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