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A Novel Compact Beamforming Network Based on Quasi-Twisted Branch Line Coupler for 5G Applications
Redundancy-Interpolated Three-Segment DAC with On-Chip Digital Calibration for Improved Static Linearity

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 16.8 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first 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, 8567 KB

Open AccessArticle

Color-to-Grayscale Image Conversion Based on the Entropy and the Local Contrast

by Lina Zhang, Jiale Yang and Yamei Xu

Electronics 2026, 15(1), 114; https://doi.org/10.3390/electronics15010114 (registering DOI) - 25 Dec 2025

Color-to-grayscale conversion is a fundamental preprocessing task with widespread applications in digital printing, electronic ink displays, medical imaging, and artistic photo stylization. A primary challenge in this domain is to simultaneously preserve global luminance distribution and local contrast. To address this, we propose [...] Read more.

Color-to-grayscale conversion is a fundamental preprocessing task with widespread applications in digital printing, electronic ink displays, medical imaging, and artistic photo stylization. A primary challenge in this domain is to simultaneously preserve global luminance distribution and local contrast. To address this, we propose an adaptive conversion method centered on a novel objective function that integrates information entropy with Edge Content (EC), a metric for local gradient information. The key advantage of our approach is its ability to generate grayscale results that maintain both rich overall contrast and fine-grained local details. Compared with previous adaptive linear methods, our approach demonstrates superior qualitative and quantitative performance. Furthermore, by eliminating the need for computationally expensive edge detection, the proposed algorithm provides an effective solution to the color-to-grayscale conversion. Full article

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

20 pages, 906 KB

Open AccessArticle

Towards Bridging GIS and 3D Modeling: A Framework for Learning Coordinate Conversion Using Machine Learning

by Thamir M. Qadah, Shema Alhazmi, Mohd Khaled Shambour, Mohammed Murad and Abdullah N. Al-Hawsawi

Electronics 2026, 15(1), 113; https://doi.org/10.3390/electronics15010113 (registering DOI) - 25 Dec 2025

Simulating what-if scenarios in 3D environments has become popular due to its potential to improve planning and operations for large-scale, complex events, such as the annual Hajj. Despite the extensive availability of spatial data through Geographic Information Systems (GIS)—information systems specialized in collecting, [...] Read more.

Simulating what-if scenarios in 3D environments has become popular due to its potential to improve planning and operations for large-scale, complex events, such as the annual Hajj. Despite the extensive availability of spatial data through Geographic Information Systems (GIS)—information systems specialized in collecting, organizing, and analyzing geospatial information—these resources remain underutilized for such use cases. Three-dimensional modelers create simulations for these use cases and build them from scratch despite the availability of GIS data, which can be tedious and error-prone. We raise the question of whether it is reasonable to use machine learning (ML) algorithms to learn coordinate conversion systems, laying the foundations for accelerating the construction of 3D environments and achieving more accurate results. In this paper, we introduce a simple yet novel framework that facilitates learning coordinate conversion systems. Using our framework, we evaluate 35 ML models and provide a detailed analysis of their prediction performance, overfitting characteristics, and trade-offs in terms of time and model size. Notably, 14 of the ML models demonstrate near-perfect learning (i.e., R-squared very close to 1.0). Furthermore, we use grid search to find better parameter settings for underperforming models. Our results demonstrate that ML can help 3D modelers automate manual tasks and improve the efficiency of 3D modeling from GIS data. Full article

(This article belongs to the Section Artificial Intelligence)

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23 pages, 5034 KB

Open AccessArticle

A3DSimVP: Enhancing SimVP-v2 with Audio and 3D Convolution

by Junfeng Yang, Mingrui Long, Hongjia Zhu, Limei Liu, Wenzhi Cao, Qin Li and Han Peng

Electronics 2026, 15(1), 112; https://doi.org/10.3390/electronics15010112 (registering DOI) - 25 Dec 2025

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a [...] Read more.

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a critical challenge. Traditional error control strategies, such as Forward Error Correction (FEC) and Automatic Repeat Request (ARQ), often introduce excessive latency or bandwidth overhead. Meanwhile, receiver-side concealment methods struggle under high motion or significant packet loss, motivating the exploration of predictive models. SimVP-v2, with its efficient convolutional architecture and Gated Spatiotemporal Attention (GSTA) mechanism, provides a strong baseline by reducing complexity and achieving competitive prediction performance. Despite its merits, SimVP-v2’s reliance on 2D convolutions for implicit temporal aggregation limits its capacity to capture complex motion trajectories and long-term dependencies. This often results in artifacts such as motion blur, detail loss, and accumulated errors. Furthermore, its single-modality design ignores the complementary contextual cues embedded in the audio stream. To overcome these issues, we propose A3DSimVP (Audio- and 3D-Enhanced SimVP-v2), which integrates explicit spatio-temporal modeling with multimodal feature fusion. Architecturally, we replace the 2D depthwise separable convolutions within the GSTA module with their 3D counterparts, introducing a redesigned GSTA-3D module that significantly improves motion coherence across frames. Additionally, an efficient audio–visual fusion strategy supplements visual features with contextual audio guidance, thereby enhancing the model’s robustness and perceptual realism. We validate the effectiveness of A3DSimVP’s improvements through extensive experiments on the KTH dataset. Our model achieves a PSNR of 27.35 dB, surpassing the 27.04 of the SimVP-v2 baseline. Concurrently, our improved A3DSimVP model reduces the loss metrics on the KTH dataset, achieving an MSE of 43.82 and an MAE of 385.73, both lower than the baseline. Crucially, our LPIPS metric is substantially lowered to 0.22. These data tangibly confirm that A3DSimVP significantly enhances both structural fidelity and perceptual quality while maintaining high predictive accuracy. Notably, A3DSimVP attains faster inference speeds than the baseline with only a marginal increase in computational overhead. These results establish A3DSimVP as an efficient and robust solution for latency-critical video applications. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)

27 pages, 12739 KB

Open AccessArticle

Unveiling Tank-Liquid Sloshing Effect on Joint Boom Stability of a Field Sprayer Under Different Running Process

by Shunzeng Wang, Zhenduo Zhang, Guoping Wang, Xianhui Zhou and Junjie Li

Electronics 2026, 15(1), 111; https://doi.org/10.3390/electronics15010111 (registering DOI) - 25 Dec 2025

The strong vibration excited by the tank-liquid sloshing of the field sprayer can result in uneven spraying, vehicle-body cartwheel, and the break of the boom during running process. So, it is crucial to investigate the stability of a field-sprayer boom under hazardous operating [...] Read more.

The strong vibration excited by the tank-liquid sloshing of the field sprayer can result in uneven spraying, vehicle-body cartwheel, and the break of the boom during running process. So, it is crucial to investigate the stability of a field-sprayer boom under hazardous operating conditions on a specified ground surface, focusing on the coupled effects of tank-liquid sloshing, boom-connection stiffness, and nozzle jetting-force characteristics. A fluid–structure interaction framework combining volume of fluid (VOF)-based sloshing simulation, finite element modeling, and full-scale experiments is developed. It is shown that high liquid-filling ratios significantly amplify transient sloshing forces during braking and swerving, inducing strong direction-dependent boom vibrations and a distinct resonance band near 50–60 Hz. Increasing connection stiffness raises natural frequencies and reduces damping, thereby enlarging vibration amplitudes. The jetting-force amplitude attenuates X-direction vibration, while frequency variation produces notable resonance excitation aligned with the harmonics of the boom. Simulation and experimental results demonstrate strong consistency, validating the proposed model. The findings reveal key coupling mechanisms governing boom stability and provide practical guidance for structural optimization and vibration suppression in field sprayers. Full article

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23 pages, 19868 KB

Open AccessArticle

Pipelined Divider with Precomputed Multiples of Divisor

by Dauren Zhexebay, Symbat Mamanova, Beibit Karibayev, Alisher Skabylov, Nursultan Meirambekuly, Gulfeiruz Ikhsan, Timur Namazbayev and Sakhybay Tynymbayev

Electronics 2026, 15(1), 110; https://doi.org/10.3390/electronics15010110 (registering DOI) - 25 Dec 2025

Division remains one of the most computationally demanding operations in digital arithmetic. Traditional algorithms, such as restoring, non-restoring, and SRT (Sweeney–Robertson–Tocher) division, are limited by sequential dependencies that reduce throughput in hardware implementations. To overcome these constraints, this work proposes a pipelined integer [...] Read more.

Division remains one of the most computationally demanding operations in digital arithmetic. Traditional algorithms, such as restoring, non-restoring, and SRT (Sweeney–Robertson–Tocher) division, are limited by sequential dependencies that reduce throughput in hardware implementations. To overcome these constraints, this work proposes a pipelined integer divider architecture that employs precomputed divisor multiples and comparator-based logic to eliminate the need for full binary adders in the quotient selection stages. The proposed design consists of a three-stage pipeline, where each stage compares the shifted partial remainder with stored multiples of the divisor (B, 2B, 3B) to generate two quotient bits per clock cycle. This approach achieves a 2× reduction in the number of computation stages compared with conventional radix-2 dividers and ensures continuous operation after an initial pipeline latency. The architecture was described in Verilog hardware description language (HDL) and implemented on a Xilinx Artix-7 (XC7A100T-1CSG324C) field-programmable gate array (FPGA) using the Xilinx ISE Design Suite 14.4. Post-synthesis simulation confirmed correct quotient and remainder generation with a maximum operating frequency of 208 MHz. The implementation occupied less than 0.3% the look-up table (LUT) resources, achieving over a twofold performance improvement compared with a non-pipelined baseline. These results demonstrate that the proposed divider provides an efficient trade-off between speed and hardware cost, making it suitable for digital signal processing and embedded computation systems. Full article

(This article belongs to the Section Microelectronics)

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28 pages, 5719 KB

Open AccessArticle

A Predictive-Reactive Learning Framework for Cellular-Connected UAV Handover in Urban Heterogeneous Networks

by Muhammad Abrar Afzal and Luis Alonso

Electronics 2026, 15(1), 109; https://doi.org/10.3390/electronics15010109 (registering DOI) - 25 Dec 2025

Unmanned aerial vehicles (UAVs) operating in dense urban environments often face link disruptions due to high mobility and interference. Reliable connectivity in such conditions requires advanced handover strategies. This paper presents a predictive-reactive Q-learning framework (PRQF) that optimizes handover decisions while sustaining throughput [...] Read more.

Unmanned aerial vehicles (UAVs) operating in dense urban environments often face link disruptions due to high mobility and interference. Reliable connectivity in such conditions requires advanced handover strategies. This paper presents a predictive-reactive Q-learning framework (PRQF) that optimizes handover decisions while sustaining throughput in dynamic heterogeneous urban networks. The framework combines an Extreme Gradient Boosting (XGBoost) classifier with a Q-learning agent through a probabilistic gating mechanism. UAVs follow a sinusoidal mobility model to ensure consistent and representative movement across experiments. Simulations using 3GPP-compliant Urban Macro (UMa) channel models in a 10 km × 10 km area show that PRQF achieves an average reduction of 84% in handovers at 100 km/h and 83% at 120 km/h, compared to the standard 3GPP A3 event-based handover method. PRQF also maintains a consistently high average throughput across all methods and speed scenarios. The results show better link stability and communication quality, demonstrating that the proposed framework is adaptable and scalable for reliable UAV communications in urban environments. Full article

(This article belongs to the Special Issue Advanced Unmanned Aerial Vehicle (UAV) Wireless Communication and Networking Technologies)

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19 pages, 10755 KB

Open AccessArticle

An Integrated Scattering Cancellation and Modification Approach for Broadband RCS Reduction of Array Antenna

by Yakun Liu, Biao Du, Dan Jia and Xuchen Han

Electronics 2026, 15(1), 108; https://doi.org/10.3390/electronics15010108 (registering DOI) - 25 Dec 2025

This paper presents a design method of integrating scattering cancellation with array-level modification techniques for broadband RCS reduction (RCSR) of an array antenna. Taking a circular patch element as an example to explain how the RCSR method is used, an L-shaped feeding structure [...] Read more.

This paper presents a design method of integrating scattering cancellation with array-level modification techniques for broadband RCS reduction (RCSR) of an array antenna. Taking a circular patch element as an example to explain how the RCSR method is used, an L-shaped feeding structure is adopted, with a dielectric substrate of Arlon Diclad 880 (tm). First, two elements with equal scattering amplitude but opposite-phase characteristics are proposed by adjusting the radiation patch dimensions and loading slots on the small patch based on characteristic mode analysis (CMA). Through arrangement of these two elements in a 2 × 2 array configuration, effective RCSR is demonstrated across 3.5–9.5 GHz. To further broaden the RCSR bandwidth, the 2 × 2 array is modified again on the ground plane using CMA. Through the integration of scattering cancellation and array-level modification techniques, a broadband RCSR design of the array antenna is realized across 2.5–11 GHz. To demonstrate the universality of the design method, 2 × 2 and 4 × 4 array antennas are designed, fabricated, and tested. The 2 × 2 array antenna can realize an average RCSR of 10.3 dB and a peak RCSR of 22 dB across 2.5–11 GHz. The 4 × 4 array antenna can realize an average RCSR of 8 dB and a peak RCSR of 23 dB across 2.5–10.5 GHz. Meanwhile, the transmission and radiation performance remains basically unchanged. The 2 × 2 array antenna works from 3.76 GHz to 5.45 GHz (36.7%) and the 4 × 4 array antenna works from 3.80 GHz to 5.30 GHz (31.1%). Their gains are 9.9 dBi for the 2 × 2 array antenna and 15.9 dBi for the 4 × 4 array antenna at 4.5 GHz. Measured results show a good agreement with calculated ones, which verifies the effectiveness and correctness of the design method. Full article

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

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18 pages, 462 KB

Open AccessArticle

Topology-Independent MAC Performance for Long-Distance UAV Swarms: Why p-Persistent Outperforms Random Backoff

by Gaoqing Shen, Bin Xie, Chen Fu and Can Wang

Electronics 2026, 15(1), 107; https://doi.org/10.3390/electronics15010107 (registering DOI) - 25 Dec 2025

Applications for intelligent cooperative Unmanned Aerial Vehicle (UAV) swarms are rapidly expanding. Efficient and reliable communication is critical for realizing this swarm intelligence, especially in remote areas lacking infrastructure where ad hoc networking is a prevalent approach. However, in such long-distance scenarios, significant [...] Read more.

Applications for intelligent cooperative Unmanned Aerial Vehicle (UAV) swarms are rapidly expanding. Efficient and reliable communication is critical for realizing this swarm intelligence, especially in remote areas lacking infrastructure where ad hoc networking is a prevalent approach. However, in such long-distance scenarios, significant propagation delays pose a fundamental challenge to Medium Access Control (MAC) protocols like carrier sense multiple access with collision avoidance (CSMA/CA). This paper theoretically compares random backoff and p-persistent to determine the optimal strategy for these conditions. We present analytical models for both strategies. The model for random backoff reveals its optimal performance is dependent on network topology, making it ill-suited for dynamic swarms. In contrast, our model for p-persistent yields an optimal transmission probability that is independent of the network topology. Simulation results validate our models, showing p-persistent achieves significantly higher throughput (over 40% improvement in an 80-node swarm). We conclude that the topology-independent characteristic of p-persistent makes it a more feasible, more robust, and superior solution for long-distance, dynamic UAV swarm networks. Full article

(This article belongs to the Section Networks)

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15 pages, 1841 KB

Open AccessArticle

RFID Tag-Integrated Multi-Sensors with AIoT Cloud Platform for Food Quality Analysis

by Zeyu Cao, Zhipeng Wu and John Gray

Electronics 2026, 15(1), 106; https://doi.org/10.3390/electronics15010106 (registering DOI) - 25 Dec 2025

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates [...] Read more.

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates the establishment of complex sensor networks to enable detailed multi-parameter monitoring of items. Despite these advancements, challenges remain in item-level sensing, data analysis, and the management of power consumption. To mitigate these shortcomings, this study presents a holistic AI-assisted, semi-passive RFID-integrated multi-sensor system designed for robust food quality monitoring. The primary contributions are threefold: First, a compact (45 mm ∗ 38 mm) semi-passive UHF RFID tag is developed, featuring a rechargeable lithium battery to ensure long-term operation and extend the readable range up to 10 m. Second, a dedicated IoT cloud platform is implemented to handle big data storage and visualization, ensuring reliable data management. Third, the system integrates machine learning algorithms (LSTM) to analyze sensing data for real-time food quality assessment. The system’s efficacy is validated through real-world experiments on food products, demonstrating its capability for low-cost, long-distance, and intelligent quality control. This technology enables low-cost, timely, and sustainable quality assessments over medium and long distances, with battery life extending up to 27 days under specific conditions. By deploying this technology, quantified food quality assessment and control can be achieved. Full article

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57 pages, 12554 KB

Open AccessArticle

Multi-Fidelity Surrogate Models for Accelerated Multi-Objective Analog Circuit Design and Optimization

by Gianluca Cornetta, Abdellah Touhafi, Jorge Contreras and Alberto Zaragoza

Electronics 2026, 15(1), 105; https://doi.org/10.3390/electronics15010105 (registering DOI) - 25 Dec 2025

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on [...] Read more.

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)

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20 pages, 3339 KB

Open AccessArticle

Packaging-Aware EMC for 2.5D/3D Semiconductor Devices with Key-Point Radiated Checks

by Lv Xin and Wang Yeliang

Electronics 2026, 15(1), 104; https://doi.org/10.3390/electronics15010104 (registering DOI) - 25 Dec 2025

Background: Electromagnetic compatibility (EMC) challenges in 2.5D/3D semiconductor packaging arise from the complex coupling between device, interposer, board, and cable domains, which are insufficiently captured by conventional board-level analysis. Method: This study proposes HiPAC-EMC, a packaging-aware EMC workflow that integrates the device, package, [...] Read more.

Background: Electromagnetic compatibility (EMC) challenges in 2.5D/3D semiconductor packaging arise from the complex coupling between device, interposer, board, and cable domains, which are insufficiently captured by conventional board-level analysis. Method: This study proposes HiPAC-EMC, a packaging-aware EMC workflow that integrates the device, package, PCB, cable harness, line impedance stabilization network (LISN), and receiver elements into an isomorphic co-model. The model mirrors the entire measurement chain and links simulation to real conducted and radiated tests. Validation: The workflow was verified using CISPR-25-compliant conducted measurements, magnetic near-field mapping, and key-point radiated checks at 3 m and 10 m, ensuring model–measurement consistency within ±2–3 dB (1σ ≈ 3.1 dB). Results: Two quantitative indices—the mitigation efficiency (η) and the common-mode hot-spot headroom (CMH)—enabled the traceable evaluation of suppression effectiveness, achieving up to 22–25 dB reduction across dominant 300–800 MHz bands. Significance: The HiPAC-EMC workflow establishes a traceable, reproducible, and measurement-faithful design methodology, providing a practical tool to de-risk EMC during early design and reduce full-band chamber time for advanced semiconductor packaging. Full article

(This article belongs to the Special Issue Advances in Semiconductor Devices and Applications)

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13 pages, 7860 KB

Open AccessArticle

A Window-Embedded Broadband Vehicle-Mounted Antenna for FM Broadcast Application Based on the Characteristic Mode Theory

by Yi Zhao, Qiqiang Li, Xianglong Liu, Pengyi Wang, Dashuang Liao, Liqiao Jing and Yongjian Cheng

Electronics 2026, 15(1), 103; https://doi.org/10.3390/electronics15010103 (registering DOI) - 25 Dec 2025

A window-embedded broadband vehicle-mounted antenna for frequency modulation (FM) broadcast application is proposed. Antenna miniaturization at sub-gigahertz frequencies remains challenging due to the inherently long wavelengths, which impose strict constraints on compactness, bandwidth, and structural weight. A promising strategy to alleviate this problem [...] Read more.

A window-embedded broadband vehicle-mounted antenna for frequency modulation (FM) broadcast application is proposed. Antenna miniaturization at sub-gigahertz frequencies remains challenging due to the inherently long wavelengths, which impose strict constraints on compactness, bandwidth, and structural weight. A promising strategy to alleviate this problem is to use the vehicle itself as an effective radiator to enhance the bandwidth and maintain good radiation performance. In this work, the potentialities of the radiation patterns offered by the vehicle are analyzed by using the characteristic mode theory (CMT). A compact T-shape coupling element, with dimensions of 0.2λ₀ × 0.08λ₀ × 0.01λ₀, is employed to simultaneously excite multiple significant characteristic modes, thereby broadening the operating band. Both simulated and measured results validate that the proposed antenna can cover the FM broadcast operating band from 87 MHz to 108 MHz, with the 1:10 scaled prototype achieving a maximum measured gain of 7.4 dBi at 950 MHz. The proposed antenna and design strategy have advantages in radio broadcasting, radio navigation, and military and law enforcement communication systems for its low-cost, compact, and easy conformal structure. Full article

(This article belongs to the Special Issue Next-Generation MIMO Systems with Enhanced Communication and Sensing)

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18 pages, 1202 KB

Open AccessArticle

A Data-Driven Distributed Autonomous Architecture for the 6G Network

by Qiuyue Gao, Jinyan Li and Yanxia Xing

Electronics 2026, 15(1), 102; https://doi.org/10.3390/electronics15010102 (registering DOI) - 25 Dec 2025

Driven by technological innovation, service diversification, and the evolution and defects of current networks, the 6th-generation (6G) network architecture is lacking in research. One of the challenges in this research is that the architectural design should take into account multiple factors: customers, operators, [...] Read more.

Driven by technological innovation, service diversification, and the evolution and defects of current networks, the 6th-generation (6G) network architecture is lacking in research. One of the challenges in this research is that the architectural design should take into account multiple factors: customers, operators, and vendors. For service-oriented and network-oriented design requirements, this article proposes a data-driven distributed autonomous architecture (DDAA) for 6G with a three-layer four-plane logical hierarchy. The architecture is simplified as four network function units (NFUs), the interaction among which is carried via dual-bus interfaces, i.e., the service-based interface (SBI) and data transmission interface (DTI). In addition, it is user data-centric and rendered as distributed autonomous domains (ADs) with different scales to better adapt to customized services. Different transition stages from the 5th generation (5G) to 6G are discussed. Network simplification evaluation is further provided by going through several signaling procedures of the 3rd-generation partnership project (3GPP), inspiring advanced research and subsequent standardization of the 6G network architecture. Full article

(This article belongs to the Special Issue 6G and Beyond: Architectures, Challenges, and Opportunities)

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19 pages, 2562 KB

Open AccessArticle

An Enhanced LSTM with Hippocampal-Inspired Episodic Memory for Urban Crowd Behavior Analysis

by Mingshou An, Hye-Youn Lim and Dae-Seong Kang

Electronics 2026, 15(1), 101; https://doi.org/10.3390/electronics15010101 (registering DOI) - 25 Dec 2025

The increasing frequency and severity of urban crowd disasters underscore a critical need for intelligent surveillance systems capable of real-time crowd anomaly detection and early warning. While deep learning models such as LSTMs, ConvLSTMs, and Transformers have been applied to video-based crowd anomaly [...] Read more.

The increasing frequency and severity of urban crowd disasters underscore a critical need for intelligent surveillance systems capable of real-time crowd anomaly detection and early warning. While deep learning models such as LSTMs, ConvLSTMs, and Transformers have been applied to video-based crowd anomaly detection, they often face limitations in long-term contextual reasoning, computational efficiency, and interpretability. To address these challenges, this paper proposes HiMeLSTM, a crowd anomaly detection framework built around a hippocampal-inspired memory-enhanced LSTM backbone that integrates Long Short-Term Memory (LSTM) networks with an Episodic Memory Unit (EMU). This hybrid design enables the model to effectively capture both short-term temporal dynamics and long-term contextual patterns essential for understanding complex crowd behavior. We evaluate HiMeLSTM on two publicly available crowd-anomaly benchmark datasets (UCF-Crime and ShanghaiTech Campus) and an in-house CrowdSurge-1K dataset, demonstrating that it consistently outperforms strong baseline architectures, including Vanilla LSTM, ConvLSTM, a lightweight spatial–temporal Transformer, and recent reconstruction-based models such as MemAE and ST-AE. Across these datasets, HiMeLSTM achieves up to 93.5% accuracy, 89.6% anomaly detection rate (ADR), and a 0.89 F1-score, while maintaining computational efficiency suitable for real-time deployment on GPU-equipped edge devices. Unlike many recent approaches that rely on multimodal sensors, optical-flow volumes, or detailed digital twins of the environment, HiMeLSTM operates solely on raw CCTV video streams combined with a simple manually defined zone layout. Furthermore, the hippocampal-inspired EMU provides an interpretable memory retrieval mechanism: by inspecting the retrieved episodes and their att ention weights, operators can understand which past crowd patterns contributed to a given decision. Overall, the proposed framework represents a significant step toward practical and reliable crowd monitoring systems for enhancing public safety in urban environments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Deep Learning for Smart Sensor and Smart Mobility)

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14 pages, 2142 KB

Open AccessArticle

Accelerating Post-Quantum Cryptography: A High-Efficiency NTT for ML-KEM on RISC-V

by Duc-Thuan Dam, Khai-Duy Nguyen, Duc-Hung Le and Cong-Kha Pham

Electronics 2026, 15(1), 100; https://doi.org/10.3390/electronics15010100 (registering DOI) - 24 Dec 2025

Post-quantum cryptography (PQC) is rapidly being standardized, with key primitives such as Key Encapsulation Mechanisms (KEMs) and Digital Signature Algorithms (DSAs) moving into practical applications. While initial research focused on pure software and hardware implementations, the focus is shifting toward flexible, high-efficiency solutions [...] Read more.

Post-quantum cryptography (PQC) is rapidly being standardized, with key primitives such as Key Encapsulation Mechanisms (KEMs) and Digital Signature Algorithms (DSAs) moving into practical applications. While initial research focused on pure software and hardware implementations, the focus is shifting toward flexible, high-efficiency solutions suitable for widespread deployment. A system-on-chip is a viable option with the ability to coordinate between hardware and software flexibly. However, the main drawback of this system is the latency in exchanging data during computation. Currently, most SoCs are implemented on FPGAs, and there is a lack of SoCs realized on ASICs. This paper introduces a complete RISC-V SoC design in an ASIC for Module Lattice-based KEM. Our system features a RISC-V processor tightly integrated with a high-efficiency Number Theoretic Transform (NTT) accelerator. This accelerator leverages custom instructions to accelerate cryptographic operations. Our research has achieved the following results: (1) The accelerator provides a speedup of up to 14.51× for NTT and 16.75× for inverse NTT operations compared to other RISC-V platforms; (2) This leads to end-to-end performance improvements for ML-KEM of up to 56.5% for security level I, 50.9% for level III, and 45.4% for level V; (3) The ASIC design is fabricated using a 180 nm CMOS process at a maximum operating frequency of 118 MHz with an area overhead of 8.7%. The chip achieved a minimum power consumption of 5.913 μW at 10 kHz and 0.9 V of supply voltage. Full article

(This article belongs to the Special Issue Recent Advances in Quantum Information)

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22 pages, 481 KB

Open AccessArticle

AIDE: An Active Inference-Driven Framework for Dynamic Evaluation via Latent State Modeling and Generative Reasoning

by Xi Chen, Changwang Liu, Chenyang Zhang, Yuxuan Wang, Jiayi Chang, Shuqing He, Wangyu Wu, Wenjun Yu and Jia Guo

Electronics 2026, 15(1), 99; https://doi.org/10.3390/electronics15010099 (registering DOI) - 24 Dec 2025

This paper introduces AIDE, an active inference-driven evaluation framework designed to provide a unified and theoretically grounded approach for analyzing sequential textual data. AIDE formulates the evaluation problem as variational inference in a latent dynamical system, enabling joint treatment of representation, temporal structure, [...] Read more.

This paper introduces AIDE, an active inference-driven evaluation framework designed to provide a unified and theoretically grounded approach for analyzing sequential textual data. AIDE formulates the evaluation problem as variational inference in a latent dynamical system, enabling joint treatment of representation, temporal structure, and predictive reasoning. The framework integrates (i) a representation and augmentation module based on variational learning and contrastive semantic encoding, (ii) a parametric state–space model that captures the evolution of latent states and supports probabilistic forecasting, and (iii) a policy-selection mechanism that minimizes the expected free energy, guiding a latent diffusion generator to produce coherent and interpretable evaluation outputs. This formulation yields a principled pipeline linking evidence accumulation, latent-state inference, and policy-driven generative reporting. Experimental studies demonstrate that AIDE provides stable inference, coherent predictions, and consistent evaluation behavior across heterogeneous textual sequences. The proposed framework offers a general probabilistic foundation for dynamic evaluation tasks and contributes a structured methodology for integrating representation learning, dynamical modeling, and generative mechanisms within a single variational paradigm. Full article

(This article belongs to the Section Artificial Intelligence)

23 pages, 933 KB

Open AccessArticle

Fast Algorithms for Small-Size Type VII Discrete Cosine Transform

by Marina Polyakova, Aleksandr Cariow and Mirosław Łazoryszczak

Electronics 2026, 15(1), 98; https://doi.org/10.3390/electronics15010098 (registering DOI) - 24 Dec 2025

This paper presents new fast algorithms for the type VII discrete cosine transform (DCT-VII) applied to input data sequences of lengths ranging from 3 to 8. Fast algorithms for small-sized trigonometric transforms enable the processing of small data blocks in image and video [...] Read more.

This paper presents new fast algorithms for the type VII discrete cosine transform (DCT-VII) applied to input data sequences of lengths ranging from 3 to 8. Fast algorithms for small-sized trigonometric transforms enable the processing of small data blocks in image and video coding with low computational complexity. To process the information in image and video coding standards, the fast DCT-VII algorithms can be used, taking into account the relationships between the DCT-VII and the type II discrete cosine transform (DCT-II). Additionally, such algorithms can be used in other digital signal processing tasks as components for constructing algorithms for large-sized transforms, leading to reduced system complexity. Existing fast odd DCT algorithms have been designed using relationships among discrete cosine transforms (DCTs), discrete sine transforms (DSTs), and the discrete Fourier transform (DFT); among different types of DCTs and DSTs; and between the coefficients of the transform matrix. However, these algorithms require a relatively large number of multiplications and additions. The process of obtaining such algorithms is difficult to understand and implement. To overcome these shortcomings, this paper applies a structural approach to develop new fast DCT-VII algorithms. The process begins by expressing the DCT-VII as a matrix-vector multiplication, then reshaping the block structure of the DCT-VII matrix to align with matrix patterns known from the basic papers in which the structural approach was introduced. If the matrix block structure does not match any known pattern, rows and columns are reordered, and sign changes are applied as needed. If this is insufficient, the matrix is decomposed into the sum of two or more matrices, each analyzed separately and transformed similarly if required. As a result, factorizations of DCT-VII matrices for different input sequence lengths are obtained. Based on these factorizations, fast DCT-VII algorithms with reduced arithmetic complexity are constructed and presented with pseudocode. To illustrate the computational flow of the resulting algorithms and their modular design, which is suitable for VLSI implementation, data-flow graphs are provided. The new DCT-VII algorithms reduce the number of multiplications by approximately 66% compared to direct matrix-vector multiplication, although the number of additions decreases by only about 6%. Full article

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

32 pages, 5130 KB

Open AccessArticle

MDB-YOLO: A Lightweight, Multi-Dimensional Bionic YOLO for Real-Time Detection of Incomplete Taro Peeling

by Liang Yu, Xingcan Feng, Yuze Zeng, Weili Guo, Xingda Yang, Xiaochen Zhang, Yong Tan, Changjiang Sun, Xiaoping Lu and Hengyi Sun

Electronics 2026, 15(1), 97; https://doi.org/10.3390/electronics15010097 (registering DOI) - 24 Dec 2025

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, [...] Read more.

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

(This article belongs to the Special Issue Advancements in Edge and Cloud Computing for Industrial IoT)

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15 pages, 400 KB

Open AccessArticle

Decentralized Control for Interrelated Systems with Asymmetric Information Architecture

by Yixing Wang, Yirun Wang, Boqun Tan, Xinghua Li and Xiao Liang

Electronics 2026, 15(1), 96; https://doi.org/10.3390/electronics15010096 (registering DOI) - 24 Dec 2025

This paper focuses on finite-horizon optimum state feedback control problems for interconnected systems of two players involved with asymmetric one-step delay information. For the finite horizon optimum decentralized control problem, a crucial and adequate condition is derived by using Pontryagin’s maximum principle. Under [...] Read more.

This paper focuses on finite-horizon optimum state feedback control problems for interconnected systems of two players involved with asymmetric one-step delay information. For the finite horizon optimum decentralized control problem, a crucial and adequate condition is derived by using Pontryagin’s maximum principle. Under this framework, player 1 transmits its state and control input data with a one-step delay to the controller of player 2, while player 1’s controller does not have access to the real-time or delayed states and control inputs of player 2, resulting in an asymmetric information structure characterized by a one-step delay Then, the solutions to the forward and backward stochastic difference equations are derived. A target tracking system is given in numerical examples to verify the proposed algorithm. Full article

(This article belongs to the Special Issue Evolutionary and Swarm Intelligence Approaches for Recommender Systems)

15 pages, 1645 KB

Open AccessArticle

Multi-Scale Convolutional Progressive Network for Fine-Grained Vehicle Recognition

by Haiming Sun, Fengxiang Jia and Xiaoqiu Zheng

Electronics 2026, 15(1), 95; https://doi.org/10.3390/electronics15010095 (registering DOI) - 24 Dec 2025

This paper proposes a solution to the challenge of low accuracy in fine-grained vehicle classification, which arises from minimal intra-class feature variations. It introduces TS-Net, a multi-scale convolutional progressive fine-grained vehicle recognition network. TS-Net utilizes a jigsaw puzzle generator to create multi-level fine-grained [...] Read more.

This paper proposes a solution to the challenge of low accuracy in fine-grained vehicle classification, which arises from minimal intra-class feature variations. It introduces TS-Net, a multi-scale convolutional progressive fine-grained vehicle recognition network. TS-Net utilizes a jigsaw puzzle generator to create multi-level fine-grained images, facilitating progressive learning from fine to coarse granularity. The network integrates multi-attention modules to localize regions of interest and optimizes feature extraction using a split-transform-fuse strategy with multi-scale convolutions. This approach enables cross-granularity feature learning, resulting in improved fine-grained vehicle classification. Experiments on the Stanford Cars dataset show that TS-Net achieves a vehicle identification accuracy of 95.68%. Ablation studies confirm the effectiveness of the progressive training strategy and highlight the synergistic contributions of each module in addressing the issue of low accuracy. Full article

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