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Acoustic Emission from GaN-on-Sapphire Structures
Machine Learning for Cybersecurity: A Survey of Applications, Adversarial Challenges, and Future Research Directions
Resistive Sensing in Soft Robotic Grippers: A Comprehensive Review of Strain, Tactile, and Ionic Sensors
Comparing Explainable AI Models: SHAP, LIME, and Their Role in Electric Field Strength Prediction over Urban Areas

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software, Microelectronics and Semiconductors and Heterogeneous Integration.
Journal Cluster of Electronic Engineering and Hardware Systems: Chips, Electronics, Hardware, Journal of Low Power Electronics and Applications, Microelectronics and Microwave.

Impact Factor: 2.6 (2024); 5-Year Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

15 pages, 1849 KB

Open AccessArticle

Digitally Adjustable Laser Diode Driver Circuit with 9 ps Resolution

by Michał Pietrzak, Wiktor Porakowski and Oleksandra Zhyhylii

Electronics 2026, 15(1), 210; https://doi.org/10.3390/electronics15010210 (registering DOI) - 1 Jan 2026

Laser pulses are essential in various scientific fields, yet existing laser diode drivers offer limited adjustability. This paper presents a digitally adjustable subnanosecond gain-switched laser diode driver, a first one with step sizes of the control being in the single-digit picosecond range. The [...] Read more.

Laser pulses are essential in various scientific fields, yet existing laser diode drivers offer limited adjustability. This paper presents a digitally adjustable subnanosecond gain-switched laser diode driver, a first one with step sizes of the control being in the single-digit picosecond range. The proposed circuit differentially drives the laser diode (LD) using two high-current gate drivers whose relative delay is digitally adjusted by a dual programmable delay line. Pulse width is defined by the delay difference between the two channels, enabling fine control without the need for high-speed semiconductor switching. Experimental results demonstrate stable optical pulse generation with widths tunable from

350 p s

to

2.8 n s

in

9 p s

increments and repetition rates exceeding

150 M H z

. Timing jitter remains below

15 p s

, and amplitude variation is below 1% across the tested operating conditions. The proposed solution provides a compact, low-cost, and highly adjustable platform for applications that require precise timing and pulse-width control, such as time-resolved measurements, range finding, and nonlinear optical excitation. Full article

(This article belongs to the Special Issue Innovations in Digital and Analog Electronic Systems for Next-Generation Devices)

16 pages, 1320 KB

Open AccessArticle

FALW-YOLOv8: A Lightweight Model for Detecting Pipeline Defects

by Huazhong Wang, Xuetao Wang and Lihua Sun

Electronics 2026, 15(1), 209; https://doi.org/10.3390/electronics15010209 (registering DOI) - 1 Jan 2026

Pipelines are critical infrastructures in both industrial production and daily life. However, defects frequently arise due to environmental and manufacturing factors, which may lead to severe safety risks. To overcome the limitations of traditional object detection methods, such as inefficient feature extraction and [...] Read more.

Pipelines are critical infrastructures in both industrial production and daily life. However, defects frequently arise due to environmental and manufacturing factors, which may lead to severe safety risks. To overcome the limitations of traditional object detection methods, such as inefficient feature extraction and the loss of critical information, this paper proposes an improved algorithm, termed FALW-YOLOv8, built upon the YOLOv8 architecture. Specifically, the FasterBlock is incorporated into the C2f module to replace standard convolutional layers, effectively reducing computational redundancy while improving feature extraction efficiency. In addition, the ADown module is employed to enhance multi-scale feature preservation, while the LSKA attention mechanism is introduced to improve detection accuracy, particularly for small defects. The Wise-IoU v2 loss function is further adopted to refine bounding box regression for complex samples. Experimental results demonstrate that the proposed FALW-YOLOv8 achieves a 5.8% improvement in mAP50, along with a 34.8% reduction in model parameters and a 30.86% decrease in computational cost. These results indicate that the proposed method achieves a favorable balance between accuracy and efficiency, making it well-suited for real-time industrial pipeline inspection applications. Full article

(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)

33 pages, 5865 KB

Open AccessArticle

Feature Selection and Fault Detection Under Dynamic Conditions of Chiller Systems

by Yashar Bezyan, Fuzhan Nasiri and Mazdak Nik-Bakht

Electronics 2026, 15(1), 208; https://doi.org/10.3390/electronics15010208 (registering DOI) - 1 Jan 2026

Faults in chiller systems can significantly reduce energy efficiency and operational performance. To address this, fault detection and diagnosis (FDD) algorithms are increasingly integrated into building management systems (BMS). This study proposes a comprehensive FDD framework addressing two key aspects: (1) fault detection [...] Read more.

Faults in chiller systems can significantly reduce energy efficiency and operational performance. To address this, fault detection and diagnosis (FDD) algorithms are increasingly integrated into building management systems (BMS). This study proposes a comprehensive FDD framework addressing two key aspects: (1) fault detection under dynamic operating conditions and (2) selection of key variables for unsupervised fault detection. Traditional approaches usually assume steady-state operation, limiting their ability to capture transient and nonlinear system behaviors. The proposed method integrates Variational Mode Decomposition (VMD) for noise reduction and signal denoising with Kernel Principal Component Analysis (KPCA) to capture nonlinear behavior in chiller systems. This combination enables accurate fault detection under both steady and transient conditions. Furthermore, a wrapper-based step-forward feature selection algorithm identifies the most informative variables for KPCA-based fault detection. Assuming at least one known fault type, the method minimizes the Missing Alarm Rate (MAR) and False Alarm Rate (FAR), enhancing adaptability to different sensor configurations. The proposed approach is validated on the ASHRAE RP-1043 dataset using first-level severity faults. Results show that the VMD-KPCA method detects 98% of faulty samples, significantly outperforming linear PCA (55%), and highlight the importance of vapor compression parameters and thermodynamic insights in improving fault detection reliability. Full article

(This article belongs to the Special Issue Advanced Fault and Error Detection Techniques Using Machine Learning and Artificial Intelligence)

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30 pages, 610 KB

Open AccessArticle

Fast DCT-VIII Algorithms for Short-Length Input Sequences

by Mateusz Raciborski, Marina Polyakova and Aleksandr Cariow

Electronics 2026, 15(1), 207; https://doi.org/10.3390/electronics15010207 (registering DOI) - 1 Jan 2026

Discrete cosine transforms (DCTs) are widely used in intelligent electronic systems for storing, processing, and transmitting data. Their popularity stems, on the one hand, from their unique properties and, on the other hand, from the availability of fast algorithms that minimize the computational [...] Read more.

Discrete cosine transforms (DCTs) are widely used in intelligent electronic systems for storing, processing, and transmitting data. Their popularity stems, on the one hand, from their unique properties and, on the other hand, from the availability of fast algorithms that minimize the computational and hardware complexity of their implementation. Until recently, the Type VIII DCT had been one of the least studied variants, with virtually no publications addressing fast algorithms for its implementation. However, this situation has changed, making the development of efficient implementation methods for this transform a timely and important research problem. In this paper, several algorithmic solutions for implementing the Type VIII DCT are proposed. A set of Type VIII DCT algorithms for small lengths N = 3, 4, 5, 6, 7 is presented. The effectiveness of the proposed solutions is due to the possibility of successful factorization of small-sized DCT-VIII matrices, leading to a reduction in the computational complexity of implementing transforms of this type. Compared with direct matrix–vector computation, the proposed algorithms achieve an approximate 53% reduction in the number of multiplications, at the cost of an increase of about 21% in the number of additions. This work continues a series of previously published studies aimed at creating a library of small-sized algorithms for discrete trigonometric transforms. Full article

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26 pages, 6034 KB

Open AccessArticle

BiLSTM-FuseNet: A Deep Fusion Model for Denoising High-Noise Near-Infrared Spectra

by Jianxia Xue, Xiaojing Chen and Soo-Hyung Kim

Electronics 2026, 15(1), 206; https://doi.org/10.3390/electronics15010206 (registering DOI) - 1 Jan 2026

Near-infrared spectroscopy (NIRS) is widely used in food, pharmaceutical, and agricultural analyses but is highly susceptible to noise. To address this, we propose BiLSTM-FuseNet, a denoising framework that combines temporal modeling and explicit noise estimation. It uses stacked Bidirectional Long Short-Term Memory (BiLSTM) [...] Read more.

Near-infrared spectroscopy (NIRS) is widely used in food, pharmaceutical, and agricultural analyses but is highly susceptible to noise. To address this, we propose BiLSTM-FuseNet, a denoising framework that combines temporal modeling and explicit noise estimation. It uses stacked Bidirectional Long Short-Term Memory (BiLSTM) layers for global–local spectral learning and an MLP branch to predict and subtract noise. Evaluated on the Tablet and AnHui soil datasets with various synthetic noise types, the model outperformed the conventional methods, achieving an RMSE of 0.024 and R² of 0.68 under mixed noise. The downstream regression improved the tablet weight prediction R² from 0.079 to 0.218. These findings demonstrate the robustness of BiLSTM-FuseNet and its clear advantages for practical downstream NIR applications. Full article

(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)

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17 pages, 921 KB

Open AccessArticle

Lightweight Kalman Spoofing Detection in Platoons of Vehicles

by Dimitrios Kosmanos, Zisis-Rafail Tzoannos, Apostolos Xenakis and Costas Chaikalis

Electronics 2026, 15(1), 205; https://doi.org/10.3390/electronics15010205 (registering DOI) - 1 Jan 2026

Spoofing attacks remain among the most critical security threats in Connected and Autonomous Vehicles (CAVs). This work introduces a lightweight, two-level spoofing detection framework based on Kalman filtering, designed for real-time deployment in vehicular platoons that communicate over Dedicated Short-Range Communications (DSRC). At [...] Read more.

Spoofing attacks remain among the most critical security threats in Connected and Autonomous Vehicles (CAVs). This work introduces a lightweight, two-level spoofing detection framework based on Kalman filtering, designed for real-time deployment in vehicular platoons that communicate over Dedicated Short-Range Communications (DSRC). At the first level, a heuristic residual-based detector identifies abnormal measurement deviations using adaptive statistical thresholds. At the second level, a Mahalanobis distance test assesses model consistency using covariance-aware anomaly scoring at a 95% confidence level. The combination of these complementary mechanisms enables both rapid alerting and robust statistical verification without the need for machine-learning training or centralized processing. Simulation results from 20 independent nodes demonstrate that the proposed approach achieves an average F1-score of 0.92 and an Area Under the ROC Curve (AUC) of 0.72, outperforming standalone detectors while maintaining low computational cost. Compared with deep learning and adaptive Extended Kalman Filter (EKF) approaches, the proposed framework achieves similar detection performance while substantially reducing computational complexity and enabling full real-time operation, making it suitable for embedded in-vehicle security modules. Full article

(This article belongs to the Special Issue Cyber Security, Design and Applications in Smart Systems)

20 pages, 6569 KB

Open AccessArticle

Cross-Modality Guided Super-Resolution for Weak-Signal Fluorescence Imaging via a Multi-Channel SwinIR Framework

by Haoxuan Huang and Hasan Abbas

Electronics 2026, 15(1), 204; https://doi.org/10.3390/electronics15010204 (registering DOI) - 1 Jan 2026

Weak-signal fluorescence channels (e.g., 4′,6-diamidino-2-phenylindole (DAPI)) often fail to provide reliable structural details due to low signal-to-noise ratio (SNR) and insufficient high-frequency information, limiting the ability of single-channel super-resolution methods to restore edge continuity and texture. This study proposes a multi-channel guided super-resolution [...] Read more.

Weak-signal fluorescence channels (e.g., 4′,6-diamidino-2-phenylindole (DAPI)) often fail to provide reliable structural details due to low signal-to-noise ratio (SNR) and insufficient high-frequency information, limiting the ability of single-channel super-resolution methods to restore edge continuity and texture. This study proposes a multi-channel guided super-resolution method based on SwinIR, utilizing the high-SNR fluorescein isothiocyanate (FITC) channel as a structural reference. Dual-channel adaptation is implemented at the model input layer, enabling the window attention mechanism to fuse cross-channel correlation information and enhance the structural recovery capability of weak-signal channels. To address the loss of high-frequency information in weak-signal imaging, we introduce a frequency-domain consistency loss: this mechanism constrains spectral consistency between the predicted and true images in the Fourier domain, improving the clarity of fine-structure reconstruction. Experimental results on the DAPI channel demonstrate significant improvements: PSNR increases from 27.05 dB to 44.98 dB, and SSIM rises from 0.763 to 0.960. Visual analysis indicates that this method restores more continuous nuclear edges and weak textural details while suppressing background noise; frequency-domain results reduce the minimum resolvable feature size from approximately 1.5 μm to 0.8 μm. In summary, multi-channel structural information provides an effective and physically interpretable deep learning approach for super-resolution reconstruction of weak-signal fluorescence images. Full article

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

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13 pages, 3784 KB

Open AccessArticle

Design and Implementation of an L-Band 400 W Continuous-Wave GaN Power Amplifier

by Xiaodong Jing, Hailong Wang, Fei You, Xiaofan Zhang and Kuo Ma

Electronics 2026, 15(1), 203; https://doi.org/10.3390/electronics15010203 (registering DOI) - 1 Jan 2026

Based on a large-signal chip model, this paper designs and implements an L-band broadband continuous-wave 400 W high-efficiency power amplifier fabricated using 0.5 μm GaN High Electron Mobility Transistor (HEMT) technology. The input-matching circuit employs a hybrid structure combining a lumped-element pre-matching network [...] Read more.

Based on a large-signal chip model, this paper designs and implements an L-band broadband continuous-wave 400 W high-efficiency power amplifier fabricated using 0.5 μm GaN High Electron Mobility Transistor (HEMT) technology. The input-matching circuit employs a hybrid structure combining a lumped-element pre-matching network and a multi-section microstrip capacitor network to achieve impedance matching with a 50 Ω port. The output-matching circuit uses a multi-segment microstrip structure to meet the impedance requirements of the continuous mode, thereby achieving broadband impedance matching. In addition, in the circuit implementation, by optimizing the placement of the blocking capacitor, the current flowing through it is minimized to a low level, enhancing the circuit’s high-power handling capability under continuous-wave operation. Additionally, the power amplifier’s reliability lifetime was calculated based on simulation results of the operating temperature of the GaN amplifier chip. Measurement results demonstrate that across a wide operating bandwidth within the L-band, the output power exceeds 400 W with a drain efficiency greater than 70%. The estimated reliability lifetime (MTTF) of the power amplifier is 8.1 × 10⁷ h. Full article

(This article belongs to the Special Issue RF/Microwave Integrated Circuits Design and Application)

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27 pages, 1978 KB

Open AccessArticle

Dynamic Task Planning for Heterogeneous Platforms via Spatio-Temporal and Capability Dual-Driven Framework

by Guangxi Zhu, Gang Wang, Wei Fu and Changxing Han

Electronics 2026, 15(1), 202; https://doi.org/10.3390/electronics15010202 (registering DOI) - 1 Jan 2026

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that [...] Read more.

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article

(This article belongs to the Section Artificial Intelligence)

21 pages, 1176 KB

Open AccessArticle

LLM-Assisted Explainable Daily Stress Recognition: Physiologically Grounded Threshold Rules from PPG Features

by Yekta Said Can

Electronics 2026, 15(1), 201; https://doi.org/10.3390/electronics15010201 (registering DOI) - 1 Jan 2026

Stress has become one of the most pervasive health challenges in modern societies, contributing to cardiovascular, cognitive, and emotional disorders that degrade overall well-being and productivity. Continuous monitoring of stress in everyday settings is thus critical for preventive healthcare. Recent advances in wearable [...] Read more.

Stress has become one of the most pervasive health challenges in modern societies, contributing to cardiovascular, cognitive, and emotional disorders that degrade overall well-being and productivity. Continuous monitoring of stress in everyday settings is thus critical for preventive healthcare. Recent advances in wearable sensing technologies, particularly photoplethysmography (PPG)-based devices, have enabled unobtrusive measurement of physiological signals linked to stress. However, the analysis of such data increasingly relies on deep learning models whose complex and non-transparent decision mechanisms limit clinical interpretability and user trust. To address this gap, this study introduces a novel LLM-assisted explainable framework that combines data-driven analysis of photoplethysmography (PPG) features with physiological reasoning. First, handcrafted cardiac variability features such as Root Mean Square of Successive Differences (RMSSD), high-frequency (HF) power, and the percentage of successive NN intervals differing by more than 50 ms (pNN50) are extracted from wearable PPG signals collected in daily conditions. After algorithmic threshold selection via ROC–Youden analysis, an LLM is used solely for physiological interpretation and literature-based justification of the resulting rules. The resulting transparent rule set achieves approximately 75% binary accuracy, rivaling CNN, LSTM, Transformer, and traditional ML baselines, while maintaining full interpretability and physiological validity. This work demonstrates that LLMs can function as scientific reasoning companions, bridging raw biosignal analytics with explainable, evidence-based models—marking a new step toward trustworthy affective computing. Full article

(This article belongs to the Special Issue What Is Next in Wearable Computing for Mental and Physical Health Monitoring?)

11 pages, 3640 KB

Open AccessArticle

Wideband 1-Bit Reconfigurable Transmitarray Using a Substrate-Integrated Cavity-Backed Patch Element

by Xiuwen Tian, Huilin Mu, Yunzhou Shi, Chunsheng Guan, Chang Ding, Lizhong Song and Baojun Song

Electronics 2026, 15(1), 200; https://doi.org/10.3390/electronics15010200 (registering DOI) - 1 Jan 2026

A novel wideband 1-bit reconfigurable transmitarray (RTA) is proposed, which is based on a substrate-integrated cavity-backed patch (SCIBP) element. The RTA element consists of a pair of SCIBP antennas, achieving wideband operational capability through the optimization of dielectric substrate thickness. To suppress surface-wave [...] Read more.

A novel wideband 1-bit reconfigurable transmitarray (RTA) is proposed, which is based on a substrate-integrated cavity-backed patch (SCIBP) element. The RTA element consists of a pair of SCIBP antennas, achieving wideband operational capability through the optimization of dielectric substrate thickness. To suppress surface-wave propagation between adjacent RTA elements, a substrate-integrated waveguide (SIW) is designed to function as a metallic isolation wall. A 180° phase shift is realized by dynamically manipulating p-i-n diodes embedded within the SCIBP antenna structure. When the dielectric substrate thickness is increased from 6 mm to 10 mm, the 3 dB transmission bandwidth is expanded from 10% to 33.6%. The simulation results confirm that the proposed element realizes a 3 dB transmission bandwidth of 33.6%. A prototype RTA with 100 elements is designed, fabricated, and measured. The prototype achieves a peak gain of 16.6 dBi at 4.6 GHz, accompanied by an aperture efficiency of 17.2% and a 3 dB gain bandwidth of 18.9%. Furthermore, measured scanned beams illustrate that the proposed RTA possesses good beamscanning performance. Owing to its many advantages, such as wideband operation, lightweight design, low cost, simple structure, and easy fabrication, it is particularly suitable for application in intelligent communication systems and radar systems. Full article

(This article belongs to the Special Issue Advanced Technologies and Future Trends in Visual Recognition and Signal Processing)

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26 pages, 8467 KB

Open AccessArticle

Low-Light Pose-Action Collaborative Network for Industrial Monitoring in Power Systems

by Qifeng Luo, Heng Zhou, Mianting Wu and Qiang Zhou

Electronics 2026, 15(1), 199; https://doi.org/10.3390/electronics15010199 (registering DOI) - 1 Jan 2026

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power [...] Read more.

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power stations. The pipeline begins with a modified Zero-DCE++ module for reference-free illumination correction, followed by pose extraction using YOLO-Pose and a novel rotation-invariant encoding of keypoints optimized for confined industrial spaces. Temporal dependencies are captured through a bidirectional LSTM network with attention mechanisms to model complex operational behaviors. We evaluate LPAC-Net on the newly curated ARID-Fall dataset, enhanced with industrial monitoring scenarios representative of electrical infrastructure environments. Experimental results demonstrate that our method outperforms state-of-the-art models, including DarkLight-R101, DTCM, FRAGNet, and URetinex-Net++, achieving 95.53% accuracy in recognizing worker activities and safety-critical events. Additional studies confirm LPAC-Net’s robustness under keypoint noise and motion blur, highlighting its practical value for intelligent monitoring in challenging industrial lighting conditions typical of underground electrical facilities and automated power stations. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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23 pages, 13339 KB

Open AccessArticle

Neural-Based Controller on Low-Density FPGAs for Dynamic Systems

by Edson E. Cruz-Miguel, José R. García-Martínez, Jorge Orrante-Sakanassi, José M. Álvarez-Alvarado, Omar A. Barra-Vázquez and Juvenal Rodríguez-Reséndiz

Electronics 2026, 15(1), 198; https://doi.org/10.3390/electronics15010198 (registering DOI) - 1 Jan 2026

This work introduces a logic resource-efficient Artificial Neural Network (ANN) controller for embedded control applications on low-density Field-Programmable Gate Array (FPGA) platforms. The proposed design relies on 32-bit fixed-point arithmetic and incorporates an online learning mechanism, enabling the controller to adapt to system [...] Read more.

This work introduces a logic resource-efficient Artificial Neural Network (ANN) controller for embedded control applications on low-density Field-Programmable Gate Array (FPGA) platforms. The proposed design relies on 32-bit fixed-point arithmetic and incorporates an online learning mechanism, enabling the controller to adapt to system variations while maintaining low hardware complexity. Unlike conventional artificial intelligence solutions that require high-performance processors or Graphics Processing Units (GPUs), the proposed approach targets platforms with limited logic, memory, and computational resources. The ANN controller was described using a Hardware Description Language (HDL) and validated via cosimulation between ModelSim and Simulink. A practical comparison was also made between Proportional-Integral-Derivative (PID) control and an ANN for motor position control. The results confirm that the architecture efficiently utilizes FPGA resources, consuming approximately

50 %

of the available Digital Signal Processor (DSP) units, less than

40 %

of logic cells, and only

6 %

of embedded memory blocks. Owing to its modular design, the architecture is inherently scalable, allowing additional inputs or hidden-layer neurons to be incorporated with minimal impact on overall resource usage. Additionally, the computational latency can be precisely determined and scales with

(16 n + 39) m + 31

clock cycles, enabling precise timing analysis and facilitating integration into real-time embedded control systems. Full article

(This article belongs to the Special Issue Embedded Systems: Fundamentals, Design and Practical Applications, 2nd Edition)

19 pages, 6054 KB

Open AccessArticle

A Smart App for the Prevention of Gender-Based Violence Using Artificial Intelligence

by Agostino Giorgio

Electronics 2026, 15(1), 197; https://doi.org/10.3390/electronics15010197 (registering DOI) - 1 Jan 2026

Gender-based violence is a widespread and persistent social scourge. The most effective strategy to reduce its impact is prevention, which has led to the adoption of a hand gesture conventionally recognized as a request for help. In addition, in cases of confirmed risk, [...] Read more.

Gender-based violence is a widespread and persistent social scourge. The most effective strategy to reduce its impact is prevention, which has led to the adoption of a hand gesture conventionally recognized as a request for help. In addition, in cases of confirmed risk, a Judge may order the potential aggressor to wear an electronic bracelet to prevent them from approaching the victim. However, these measures have proven largely insufficient, as incidents of gender-based violence continue to recur. To address this limitation, the author developed an application, named “no pAIn app”, based on artificial intelligence (AI), designed to create a virtual shield for potential victims. The app, which can run on both smartphones and smartwatches, automatically sends help requests with geolocation data when AI detects a real danger situation. The process is fully autonomous and does not require any user intervention, ensuring fast, discreet, and reliable assistance even when the victim cannot act directly. Scenario-based tests in realistic domestic environments showed that configured danger keywords were reliably detected in the vast majority of test cases, with end-to-end alert delivery typically completed within two seconds. Preliminary battery profiling indicated approximately 5% consumption over 24 h of continuous operation confirming the feasibility of long-term daily use. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)

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9 pages, 189 KB

Open AccessEditorial

Advances in Computational Intelligence and Machine Learning Models and Applications

by Grzegorz Dudek and Arkadiusz Tomczyk

Electronics 2026, 15(1), 196; https://doi.org/10.3390/electronics15010196 (registering DOI) - 1 Jan 2026

Machine learning (ML) and artificial intelligence (AI) have entered a phase of accelerated evolution, reshaping the computational landscape and influencing an ever-growing spectrum of scientific and industrial activities [...] Full article

(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)

22 pages, 6658 KB

Open AccessArticle

Robust Synergistic Control Architecture for High-Frequency Resonance Suppression in Precision Linear Motion Stages

by Huairong Chen and Feng Gao

Electronics 2026, 15(1), 195; https://doi.org/10.3390/electronics15010195 (registering DOI) - 1 Jan 2026

In high-precision positioning applications, lightly damped structural resonances fundamentally limit the achievable performance of precision linear motion stages, enforcing a stringent trade-off between control bandwidth and transient vibration suppression. This paper investigates a unified synergistic control architecture integrating input shaping (IS), feedforward control [...] Read more.

In high-precision positioning applications, lightly damped structural resonances fundamentally limit the achievable performance of precision linear motion stages, enforcing a stringent trade-off between control bandwidth and transient vibration suppression. This paper investigates a unified synergistic control architecture integrating input shaping (IS), feedforward control (FF), and notch filtering (NF) within a feedback framework to jointly mitigate command-induced excitation, compensate predictive dynamics, and suppress narrowband resonant modes. Four representative schemes—FB, FB+NF, FF+FB+NF, and IS+FF+FB+NF—are systematically evaluated through step/ramp responses, sinusoidal tracking, and industrial S-curve trajectory under aggressive operating conditions (up to 2.8 m/s and 160 m/s²). Simulation results show that incorporating IS into the FF+FB+NF baseline effectively eliminates overshoot and suppresses residual vibration, yielding a 22.0% reduction in the ±2 μm settling time. Complementary frequency-domain analyses demonstrate that the proposed IS+FF+FB+NF architecture achieves a superior balance between tracking agility and stability, maintaining robust gain/phase margins while attenuating resonant sensitivity peaks. Robustness studies further indicate that the proposed IS+FF+FB+NF architecture preserves bandwidth consistency despite resonant frequency drifts. Overall, this coordinated integration provides a practically deployable and industrially compatible solution for enhancing vibration suppression and positioning consistency in precision motion systems. Full article

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

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18 pages, 10520 KB

Open AccessArticle

Design and Optimization of LTCC Broadband Interconnect Structures for Bare-Chip Integration

by Junhao Yao, Shenglin Yu, Jianlin Huang and Chunlei Chen

Electronics 2026, 15(1), 194; https://doi.org/10.3390/electronics15010194 (registering DOI) - 1 Jan 2026

In bare-die integration based on low-temperature co-fired ceramic (LTCC) multilayer interconnects, broadband signal transmission is often limited by impedance mismatch, parasitic effects introduced by gold-wire bonding, and discontinuities in interlayer transitions. These issues collectively form major bottlenecks for achieving low-loss and wideband interconnections [...] Read more.

In bare-die integration based on low-temperature co-fired ceramic (LTCC) multilayer interconnects, broadband signal transmission is often limited by impedance mismatch, parasitic effects introduced by gold-wire bonding, and discontinuities in interlayer transitions. These issues collectively form major bottlenecks for achieving low-loss and wideband interconnections in high-frequency LTCC modules. To address these challenges, this paper proposes a multi-layer collaborative optimization framework for LTCC bare-die interconnects operating from 1 to 20 GHz. The proposed framework jointly considers impedance matching, bonding parameter optimization, and interlayer transition enhancement to achieve broadband and high-performance signal transmission. First, two T-type microstrip matching networks are designed based on the complex input impedance of the bare die, and their parameters are optimized using ADS (an integrated circuit design software, version 2020). Second, a microstrip–gold-wire bond–bare-die interconnect model is established to analyze bonding-induced parasitic effects, revealing that a bond center spacing of 0.12 mm provides optimal high-frequency performance. Third, for the stripline–via–microstrip transition, a coaxial-like via structure combined with a square defected ground structure (DGS) is introduced to improve impedance continuity and electromagnetic field confinement. Full-path cascaded simulations demonstrate that the proposed interconnect achieves a return loss better than −23.1 dB and an insertion loss below 0.45 dB across the 1–20 GHz frequency range. Compared with conventional LTCC interconnect structures, the proposed method improves return loss by more than 7 dB and reduces insertion loss by approximately 0.12 dB. The results confirm the effectiveness of the proposed collaborative optimization strategy and provide reusable design guidelines for broadband bare-die integration in high-frequency LTCC multilayer modules. Full article

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13 pages, 10544 KB

Open AccessArticle

Stability-Guaranteed Grant-Free Access for Cyber–Physical System over Space–Air–Ground Integrated Networks

by Xiaoyang Wang, Wei Li, Zhiyu Li, Dan Liu, Guangchuan Pan and Yan Wu

Electronics 2026, 15(1), 193; https://doi.org/10.3390/electronics15010193 (registering DOI) - 1 Jan 2026

In this paper, we investigate the grant-free (GF) accessing for cyber–physical systems (CPSs) over space–air–ground integrated networks (SAGINs) by jointly considering system stability and power consumption. The problem of GF access for CPSs over SAGINs is modeled as a Markov decision process where [...] Read more.

In this paper, we investigate the grant-free (GF) accessing for cyber–physical systems (CPSs) over space–air–ground integrated networks (SAGINs) by jointly considering system stability and power consumption. The problem of GF access for CPSs over SAGINs is modeled as a Markov decision process where preamble sequences are chosen to minimize power consumption while guaranteeing system stability. To solve this problem, a distributed multi-agent deep reinforcement learning framework based on factorization technology is proposed. In addition, a local network based on hierarchical reinforcement learning is designed to prevent the explosion of the dimension of the action space, in turn reducing the computational complexity of the proposed algorithm. Finally, the simulation results validate the performance superiority of the proposed scheme in terms of convergence, power consumption and stability compared with the baseline schemes. Full article

(This article belongs to the Special Issue Applied Cryptography and Practical Cryptoanalysis for Web 3.0, Volume 2)

13 pages, 2385 KB

Open AccessArticle

DSCM: A Dominance-Based Subgraph Counting Method

by Heming Yu, Ji Li, Jiaquan Li and Chuanwen Li

Electronics 2026, 15(1), 192; https://doi.org/10.3390/electronics15010192 (registering DOI) - 1 Jan 2026

Graphs are widely used to represent vertices and their relationships, with the subgraph counting problem being a crucial challenge in network analysis. Originating from subgraph matching, this problem aims to efficiently determine the number of subgraphs in a data graph that are isomorphic [...] Read more.

Graphs are widely used to represent vertices and their relationships, with the subgraph counting problem being a crucial challenge in network analysis. Originating from subgraph matching, this problem aims to efficiently determine the number of subgraphs in a data graph that are isomorphic to a given query graph. Since this task is NP-hard, existing approaches are typically divided into exact and approximate methods. Exact methods compute the precise number of isomorphic subgraphs but incur prohibitive computational costs on large graphs. Approximate methods, often based on Graph Neural Networks (GNNs), estimate the subgraph count more efficiently but tend to suffer when there is a large size discrepancy between the data graph and the query graph, leading to inaccurate vertex matching. To address these limitations, we propose a domination-based subgraph counting method (DSCM), which improves vertex matching by using a vertex dominance embedding method. This approach, combined with an attention mechanism, accurately computes subgraph counts. Experimental results show that DSCM outperforms existing methods, providing an efficient solution for subgraph counting in large graphs. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 3394 KB

Open AccessArticle

Wide Beam Analysis of Phased EM Surfaces

by Jiayue He, Fan Yang, Xiaotao Xu and Shenheng Xu

Electronics 2026, 15(1), 191; https://doi.org/10.3390/electronics15010191 - 31 Dec 2025

Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth [...] Read more.

Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth to the surface’s core physical parameters. A direct scaling equation is first derived for an idealized continuous aperture, revealing a proportionality among beamwidth, the quadratic phase coefficient, and aperture size, which demonstrates the potential for quasi-omnidirectional coverage. The model is then extended to practical scenarios, showing that the main-lobe taper is directly controlled by the aperture amplitude taper, establishing a decoupling principle for independent control of beam shape and width. Finally, by modeling the array factor of a discrete aperture, the trade-off between element spacing and maximum beamwidth is quantified, providing clear design rules to prevent grating lobe distortion. This work provides an intuitive, physics-based foundation for the systematic design and performance prediction of wide-beam phased EM surfaces. Full article

(This article belongs to the Special Issue Advanced Antennas and Propagation for Next-Gen Wireless)

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