Electronics

19 pages, 4034 KB

Open AccessArticle

Assessment of a Hybrid Modulation Strategy for Asymmetrical Cascaded Multilevel Inverters Under Comparative Analysis

by Gerlando Frequente, Massimo Caruso, Giuseppe Schettino and Rosario Miceli

Electronics 2025, 14(21), 4354; https://doi.org/10.3390/electronics14214354 - 6 Nov 2025

Viewed by 341

This paper presents a novel hybrid modulation technique for Asymmetrical Cascaded H-Bridge Multilevel Inverters (ACHBMLIs), specifically designed to enhance both efficiency and harmonic performance. Unlike conventional strategies, the proposed method optimizes the switching scheme by operating the high-voltage H-Bridge at the fundamental frequency, [...] Read more.

This paper presents a novel hybrid modulation technique for Asymmetrical Cascaded H-Bridge Multilevel Inverters (ACHBMLIs), specifically designed to enhance both efficiency and harmonic performance. Unlike conventional strategies, the proposed method optimizes the switching scheme by operating the high-voltage H-Bridge at the fundamental frequency, thereby significantly reducing switching losses while maintaining low harmonic distortion levels comparable to traditional Pulse Width Modulation (PWM). To assess the effectiveness of the approach, a comprehensive comparison was conducted against two widely adopted modulation techniques for ACHBMLIs: Multicarrier Pulse Width Modulation (MPWM) and the Staircase Modulation Strategy (SMS). The evaluation involved both simulation and real-time Hardware-in-the-Loop (HIL) testing of a 7-level three-phase ACHBMLI, with a focus on key performance indicators such as voltage and current harmonic distortion, as well as converter efficiency. The results demonstrate that the proposed hybrid modulation achieves higher efficiency than PWM and lower current Total Harmonic Distortion (THD) than SMS. These findings highlight the potential of the hybrid strategy as a compelling solution for applications that demand an optimal balance between energy efficiency and waveform quality. Full article

(This article belongs to the Special Issue Power Electronics and Renewable Energy System)

► Show Figures

Graphical abstract

12 pages, 3489 KB

Open AccessArticle

Rapid Thermal Annealing for Reduced Leakage and Enhanced Endurance of Reactive-Sputtered AlScN-Based Ferroelectric Memory Capacitors

by Juno Bae, Yoojin Lim, Jong Min Park, Hyeong Jun Joo and Geonwook Yoo

Electronics 2025, 14(21), 4353; https://doi.org/10.3390/electronics14214353 - 6 Nov 2025

Viewed by 572

Abstract

In this study, we investigate the effects of rapid thermal annealing (RTA) in a nitrogen ambient on Al_0.8Sc_0.2N metal–ferroelectric–metal capacitors. The RTA treatment of up to 13 min on an as-deposited AlScN film markedly improves electrical reliability while maintaining [...] Read more.

In this study, we investigate the effects of rapid thermal annealing (RTA) in a nitrogen ambient on Al_0.8Sc_0.2N metal–ferroelectric–metal capacitors. The RTA treatment of up to 13 min on an as-deposited AlScN film markedly improves electrical reliability while maintaining remanent polarization largely unchanged. The leakage current density decreases from 152.63 to 71.37 mA/cm², and endurance increases to 5000 cycles. X-ray diffraction analysis reveals enhanced crystalline and improved c-axis orientation, which mitigates grain-boundary defects and suppresses leakage pathways. The RTA promotes Pt diffusion, resulting in an 11% increase in the dielectric constant. Moreover, it introduces tensile strain that reduces the coercive field by lowering the ferroelectric switching barrier. These findings indicate that the RTA process in a nitrogen atmosphere is an effective approach for improving the quality of AlScN thin film, thereby supporting the development of its reliable ferroelectric devices. Full article

► Show Figures

Figure 1

23 pages, 2098 KB

Open AccessArticle

Cooperative NOMA with RIS Assistance for Short-Packet Communications Under Hardware Impairments

by Wenbin Song, Dechuan Chen, Jin Li, Xingang Zhang and Zhipeng Wang

Electronics 2025, 14(21), 4352; https://doi.org/10.3390/electronics14214352 - 6 Nov 2025

Viewed by 393

Abstract

Ultra-reliable low-latency communication (URLLC) presents significant challenges in simultaneously guaranteeing stringent latency bounds, ultra-high reliability, and efficient resource utilization under dynamic channel conditions. To address these joint constraints, a novel framework that integrates a reconfigurable intelligent surface (RIS) with cooperative non-orthogonal multiple access [...] Read more.

Ultra-reliable low-latency communication (URLLC) presents significant challenges in simultaneously guaranteeing stringent latency bounds, ultra-high reliability, and efficient resource utilization under dynamic channel conditions. To address these joint constraints, a novel framework that integrates a reconfigurable intelligent surface (RIS) with cooperative non-orthogonal multiple access (NOMA) is proposed for short-packet communications. Two distinct phase configuration designs for the RIS are considered, i.e., a near-user priority strategy (NUPS) and a far-user priority strategy (FUPS). The NUPS configures the RIS to enhance the received signal power for the near user, while the FUPS optimizes the phase shifts to maximize the received power for the far user. Closed-form expressions that characterize the average block error rate (BLER) of the near and far users under the two proposed strategies in the presence of hardware impairments are derived. Specifically, the analysis for the far user considers both selection combining (SC) and maximum ratio combining (MRC) reception schemes. Based on the average BLER, we then derive a closed-form expression for the effective throughput. Simulation findings reveal the following: (1) The far user in the proposed cooperative NOMA achieves a lower average BLER than in the non-cooperative NOMA. (2) When the RIS is deployed in close proximity to the base station (BS), the NUPS can effectively leverage the RIS to enhance the far user’s signal quality through cooperation, without sacrificing the near user’s priority; and (3) SC serves as a low-complexity alternative that achieves near-optimal performance when inter-user channel conditions are favorable. Full article

(This article belongs to the Special Issue Next-Generation Intelligent Wireless Communication Systems: Theory, Algorithms, and Deployable Applications)

► Show Figures

Figure 1

31 pages, 5169 KB

Open AccessArticle

Harmonic Mitigation in Unbalanced Grids Using Hybrid PSO-GA Tuned PR Controller for Two-Level SPWM Inverter

by Pema Dorji, Taimoor Muzaffar Gondal, Stefan Lachowicz and Octavian Bass

Electronics 2025, 14(21), 4351; https://doi.org/10.3390/electronics14214351 - 6 Nov 2025

Viewed by 518

Abstract

This study proposes an integrated control–optimization framework for harmonic mitigation in two-level, grid-connected inverters with battery energy storage operating under unbalanced grid conditions. A proportional–resonant controller in the stationary

α β

frame and a proportional–integral controller in the synchronous

d q

frame are [...] Read more.

This study proposes an integrated control–optimization framework for harmonic mitigation in two-level, grid-connected inverters with battery energy storage operating under unbalanced grid conditions. A proportional–resonant controller in the stationary

α β

frame and a proportional–integral controller in the synchronous

d q

frame are compared, with controller gains optimized using PSO, GA, and a hybrid PSO–GA approach. The hybrid method achieves superior trade-offs among THD, convergence speed, and computational effort. For the PR controller, hybrid PSO–GA reduces THD to 1.07%, satisfying IEEE 1547 and IEC 61727 standards, while for the PI controller it achieves 2.70%, outperforming standalone PSO (4.12%) and GA (3.38%). The hybrid-optimized gains further minimize tracking error indices (IAE, ISE, ITAE, ITSE), ensuring precise steady-state current regulation. Convergence analysis shows that hybrid PSO–GA attains optimal solutions within three iterations for both controllers, faster than GA and comparable to PSO for the PR case. Simulation studies on the IEEE 13-bus unbalanced feeder in DIgSILENT PowerFactory validate the proposed framework. Results confirm that the PR controller delivers a 60.36% THD reduction and tenfold ISE improvement over the optimized PI design, establishing a robust and scalable solution for harmonic suppression in unbalanced grid-tied energy systems. Full article

(This article belongs to the Special Issue AI-Driven Energy Optimization, Diagnosis, and Control for Next-Generation Electric Vehicles)

► Show Figures

Figure 1

30 pages, 658 KB

Open AccessArticle

Quantitative Metrics for Balancing Privacy and Utility in Pseudonymized Big Data

by Soonseok Kim

Electronics 2025, 14(21), 4350; https://doi.org/10.3390/electronics14214350 - 6 Nov 2025

Viewed by 434

Abstract

The increasing demand for data utilization has renewed attention to the trade-off between privacy protection and data utility, particularly concerning pseudonymized datasets. Traditional methods for evaluating re-identification risk and utility often rely on fragmented and incompatible metrics, complicating the assessment of the overall [...] Read more.

The increasing demand for data utilization has renewed attention to the trade-off between privacy protection and data utility, particularly concerning pseudonymized datasets. Traditional methods for evaluating re-identification risk and utility often rely on fragmented and incompatible metrics, complicating the assessment of the overall effectiveness of pseudonymization strategies. This study proposes a novel quantitative metric—Relative Utility–Threat (RUT)—which enables the integrated evaluation of safety (privacy) and utility in pseudonymized data. Our method transforms various risk and utility metrics into a unified probabilistic scale (0–1), facilitating standardized and interpretable comparisons. Through scenario-based analyses using synthetic datasets that reflect different data distributions (balanced, skewed, and sparse), we demonstrate how variations in pseudonymization intensity influence both privacy and utility. The results indicate that certain data characteristics significantly affect the balance between protection and usability. This approach relies on simple, lightweight computations—scanning the data once, grouping similar records, and comparing their distributions. Because these operations naturally parallelize in distributed environments such as Spark, the proposed framework can efficiently scale to large pseudonymized datasets. Full article

► Show Figures

Figure 1

34 pages, 11286 KB

Open AccessArticle

Degradation of Multi-Task Prompting Across Six NLP Tasks and LLM Families

by Federico Di Maio and Manuel Gozzi

Electronics 2025, 14(21), 4349; https://doi.org/10.3390/electronics14214349 - 6 Nov 2025

Viewed by 788

Abstract

This study investigates how increasing prompt complexity affects the performance of Large Language Models (LLMs) across multiple Natural Language Processing (NLP) tasks. We introduce an incremental evaluation framework where six tasks—JSON formatting, English-Italian translation, sentiment analysis, emotion classification, topic extraction, and named entity [...] Read more.

This study investigates how increasing prompt complexity affects the performance of Large Language Models (LLMs) across multiple Natural Language Processing (NLP) tasks. We introduce an incremental evaluation framework where six tasks—JSON formatting, English-Italian translation, sentiment analysis, emotion classification, topic extraction, and named entity recognition—are progressively combined within a single prompt. Six representative open-source LLMs from different families (Llama 3.1 8B, Gemma 3 4B, Mistral 7B, Qwen3 4B, Granite 3.1 3B, and DeepSeek R1 7B) were systematically evaluated using local inference environments to ensure reproducibility. Results show that performance degradation is highly architecture-dependent: while Qwen3 4B maintained stable performance across all tasks, Gemma 3 4B and Granite 3.1 3B exhibited severe collapses in fine-grained semantic tasks. Interestingly, some models (e.g., Llama 3.1 8B and DeepSeek R1 7B) demonstrated positive transfer effects, improving in certain tasks under multitask conditions. Statistical analyses confirmed significant differences across models for structured and semantic tasks, highlighting the absence of a universal degradation rule. These findings suggest that multitask prompting resilience is shaped more by architectural design than by model size alone, and they motivate adaptive, model-specific strategies for prompt composition in complex NLP applications. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)

► Show Figures

Figure 1

23 pages, 2699 KB

Open AccessArticle

Data Secure Storage Mechanism for Trustworthy Data Space

by Xinyi Yang, Qicheng Luo, Jiang Xu and Qinghong Cao

Electronics 2025, 14(21), 4348; https://doi.org/10.3390/electronics14214348 - 6 Nov 2025

Viewed by 500

Abstract

In today’s rapidly evolving data environment, secure and efficient storage solutions are fundamental to supporting the robust development of the data economy. Trustworthy data space serves as an innovative technological framework for addressing critical challenges in data circulation. It is specifically designed to [...] Read more.

In today’s rapidly evolving data environment, secure and efficient storage solutions are fundamental to supporting the robust development of the data economy. Trustworthy data space serves as an innovative technological framework for addressing critical challenges in data circulation. It is specifically designed to facilitate the secure exchange in data elements and overcome trust barriers in cross-organizational data sharing. However, current decentralized storage architectures still have significant implementation gaps. Practical deployment and system integration remain substantial challenges for existing technological solutions. To address these issues, this paper first conducts a systematic analysis of existing trusted data storage methods. On this basis, it proposes a data-secure storage mechanism based on polynomial commitment. This mechanism uses polynomial commitment to implement data storage and verification, thereby ensuring data integrity and consistency. Meanwhile, it integrates homomorphic signature technology to guarantee the authenticity of data sources without disclosing original data. Additionally, a data modification recording function is introduced to ensure the traceability of all operations. Experimental results show that the proposed scheme achieves superior performance in three key aspects: communication overhead, storage efficiency, and data update costs. Full article

(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)

► Show Figures

Figure 1

15 pages, 5351 KB

Open AccessArticle

A Steganalysis Method Based on Relationship Mining

by Ruiyao Yang, Yu Yang, Linna Zhou and Xiangli Meng

Electronics 2025, 14(21), 4347; https://doi.org/10.3390/electronics14214347 - 6 Nov 2025

Viewed by 355

Abstract

Steganalysis is a critical research direction in the field of information security. Traditional approaches typically employ convolution operations for feature extraction, followed by classification on noise residuals. However, since steganographic signals are inherently weak, convolution alone cannot fully capture their characteristics. To address [...] Read more.

Steganalysis is a critical research direction in the field of information security. Traditional approaches typically employ convolution operations for feature extraction, followed by classification on noise residuals. However, since steganographic signals are inherently weak, convolution alone cannot fully capture their characteristics. To address this limitation, we propose a steganalysis method based on relationship mining, termed RMNet, which leverages positional relationships of steganographic signals for detection. Specifically, features are modeled as graph nodes, where both locally focused and globally adaptive dynamic adjacency matrices guide the propagation paths of these nodes. Meanwhile, the results are further constrained in the feature space, encouraging intra-class compactness and inter-class separability, thereby increasing inter-class separability of positional features and yielding a more discriminative decision boundary. Additionally, to counter signal attenuation during network propagation, we introduce a multi-scale perception module with cross-attention fusion. Experimental results demonstrate that RMNet achieves performance comparable to state-of-the-art models on the BOSSbase and BOWS2 datasets, while offering superior generalization capability. Full article

(This article belongs to the Topic Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence)

► Show Figures

Figure 1

21 pages, 1297 KB

Open AccessArticle

Neural Network-Aided Hybrid Particle/FIR Filter for Indoor Localization Using Wireless Sensor Networks

by Jung Min Pak

Electronics 2025, 14(21), 4346; https://doi.org/10.3390/electronics14214346 - 6 Nov 2025

Viewed by 293

Abstract

Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a [...] Read more.

Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a diminishing sample diversity leads to failures under various conditions. Hence, this paper proposes a novel hybrid localization algorithm that combines a PF, a finite impulse response (FIR) filter, and an artificial neural network. In the proposed algorithm, the PF serves as the main filter for localization because it performs excellently in nonlinear, non-Gaussian systems under normal operation. The neural network is trained to classify whether the system is operating normally or experiencing a failure, based on estimation results from the PF. If a PF failure is detected by the network, the assisting FIR filter is activated to recover the PF from failures. The localization accuracy and reliability of the proposed neural network-aided hybrid particle/FIR filter are confirmed via comparisons with existing algorithms. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

► Show Figures

Figure 1

25 pages, 3041 KB

Open AccessArticle

Renewable-Aware Container Migration in Multi-Data Centers

by Xiong Fu, Zhangchi Ma, Xuezheng Shao, Guo Chen and Ji Qi

Electronics 2025, 14(21), 4345; https://doi.org/10.3390/electronics14214345 - 6 Nov 2025

Cited by 1 | Viewed by 561

Abstract

The proliferation of artificial intelligence (AI) and online services has significantly escalated the demand for computing and storage resources, which are fundamentally enabled by cloud computing infrastructure. As the backbone of cloud computing services, data centers have undergone continuous expansion in scale, consequently [...] Read more.

The proliferation of artificial intelligence (AI) and online services has significantly escalated the demand for computing and storage resources, which are fundamentally enabled by cloud computing infrastructure. As the backbone of cloud computing services, data centers have undergone continuous expansion in scale, consequently leading to significant energy consumption and a significant carbon footprint. To effectively mitigate the environmental impact, the strategy should prioritize the integration of renewable energy, while simultaneously minimizing other contributing factors such as energy consumption. Achieving both objectives simultaneously requires a fine-grained, dynamic approach to workload management. To this end, this study proposes a comprehensive container placement strategy that integrates a dynamic priority-based container selection algorithm with a multi-factor single-objective container placement algorithm based on the Dream Optimization Algorithm (DOA). The placement algorithm converts multiple factors—including load balancing in multi-data center environments, energy consumption, renewable energy utilization rate, carbon emissions, Service Level Agreement Violation (SLAV), and container migration costs—into a comprehensive fitness metric. Experimental results on Google and Alibaba datasets show our method consistently achieves the highest renewable energy utilization rates (up to 92.08%) and the lowest carbon emissions. Furthermore, our integrated strategy demonstrates a superior trade-off, reducing migration counts by up to 16.3% and SLAV by 12.4% compared to baselines, while maintaining excellent green performance. This establishes our method as a practical and effective solution for sustainable cloud computing. Full article

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

► Show Figures

Figure 1

20 pages, 1597 KB

Open AccessArticle

Three-Level MIFT: A Novel Multi-Source Information Fusion Waterway Tracking Framework

by Wanqing Liang, Chen Qiu, Mei Wang and Ruixiang Kan

Electronics 2025, 14(21), 4344; https://doi.org/10.3390/electronics14214344 - 5 Nov 2025

Viewed by 347

Abstract

To address the limitations of single-sensor perception in inland vessel monitoring and the lack of robustness of traditional tracking methods in occlusion and maneuvering scenarios, this paper proposes a hierarchical multi-target tracking framework that fuses Light Detection and Ranging (LiDAR) data with Automatic [...] Read more.

To address the limitations of single-sensor perception in inland vessel monitoring and the lack of robustness of traditional tracking methods in occlusion and maneuvering scenarios, this paper proposes a hierarchical multi-target tracking framework that fuses Light Detection and Ranging (LiDAR) data with Automatic Identification System (AIS) information. First, an improved adaptive LiDAR tracking algorithm is introduced: stable trajectory tracking and state estimation are achieved through hybrid cost association and an Adaptive Kalman Filter (AKF). Experimental results demonstrate that the LiDAR module achieves a Multi-Object Tracking Accuracy (MOTA) of 89.03%, an Identity F1 Score (IDF1) of 89.80%, and an Identity Switch count (IDSW) as low as 5.1, demonstrating competitive performance compared with representative non-deep-learning-based approaches. Furthermore, by incorporating a fusion mechanism based on improved Dempster–Shafer (D-S) evidence theory and Covariance Intersection (CI), the system achieves further improvements in MOTA (90.33%) and IDF1 (90.82%), while the root mean square error (RMSE) of vessel size estimation decreases from 3.41 m to 1.97 m. Finally, the system outputs structured three-level tracks: AIS early-warning tracks, LiDAR-confirmed tracks, and LiDAR-AIS fused tracks. This hierarchical design not only enables beyond-visual-range (BVR) early warning but also enhances perception coverage and estimation accuracy. Full article

► Show Figures

Figure 1

24 pages, 666 KB

Open AccessArticle

LLMLoc: A Structure-Aware Retrieval System for Zero-Shot Bug Localization

by Gyumin Nam and Geunseok Yang

Electronics 2025, 14(21), 4343; https://doi.org/10.3390/electronics14214343 - 5 Nov 2025

Viewed by 475

Abstract

Bug localization is a critical task in large-scale software maintenance, as it reduces exploration costs and enhances system reliability. However, existing approaches face limitations due to semantic mismatches between bug reports and source code, insufficient use of structural information, and instability in candidate [...] Read more.

Bug localization is a critical task in large-scale software maintenance, as it reduces exploration costs and enhances system reliability. However, existing approaches face limitations due to semantic mismatches between bug reports and source code, insufficient use of structural information, and instability in candidate rankings. To address these challenges, this paper proposes LLMLoc, a system that integrates traditional statistical methods with semantic retrieval, centered on a Structure-Aware Semantic Retrieval (SASR) framework. Experiments on all 835 bugs from the Defects4J dataset show that LLMLoc achieves relative improvements of 3.4 percentage points in Mean Average Precision (MAP) and 29.8 percent in Mean Reciprocal Rank (MRR) compared with state-of-the-art LLM-based methods. These results show that combining structural cues with semantic representations provides more effective retrieval than relying on LLM inference alone. Furthermore, by stabilizing Top-K candidate sets, LLMLoc reduces ranking instability and delivers practical benefits even in real-world maintenance environments with insufficient testing resources. Full article

► Show Figures

Figure 1

18 pages, 6335 KB

Open AccessArticle

Real-Time Estimation of Ionospheric Power Spectral Density for Enhanced BDS PPP/PPP-AR Performance

by Yixi Wang, Huizhong Zhu, Qi Xu, Jun Li and Chuanfeng Song

Electronics 2025, 14(21), 4342; https://doi.org/10.3390/electronics14214342 - 5 Nov 2025

Viewed by 276

Abstract

The undifferenced and uncombined (UDUC) model preserves raw code and carrier-phase observations for each frequency, avoiding differencing or ionosphere-free combinations. This approach enables the direct estimation of atmospheric parameters. However, the stochastic characteristics of these parameters, particularly ionospheric delay, are often oversimplified or [...] Read more.

The undifferenced and uncombined (UDUC) model preserves raw code and carrier-phase observations for each frequency, avoiding differencing or ionosphere-free combinations. This approach enables the direct estimation of atmospheric parameters. However, the stochastic characteristics of these parameters, particularly ionospheric delay, are often oversimplified or based on empirical assumptions, limiting the accuracy and convergence speed of Precise Point Positioning (PPP). To address this issue, this study introduces a stochastic constraint model based on the power spectral density (PSD) of ionospheric variations. The PSD describes the distribution of ionospheric delay variance across temporal frequencies, thereby providing a physically meaningful constraint for modeling their temporal correlations. Integrating this PSD-derived stochastic model into the UDUC framework improves both ionospheric delay estimation and PPP performance, especially under disturbed ionospheric conditions. This paper presents a BDS PPP/PPP-AR method that estimates the ionospheric power spectral density (IPSD) in real time. Vondrak smoothing is applied to suppress noise in ionospheric observations before IPSD estimation. Experimental results demonstrate that the proposed approach significantly improves convergence time and positioning accuracy. Compared to the empirical IPSD model, the PPP mode using the estimated IPSD reduced horizontal and vertical convergence times by 11.1% and 13.2%, and improved the corresponding accuracies by 15.7% and 12.6%, respectively. These results confirm that real-time IPSD estimation, coupled with Vondrak smoothing, establishes an adaptive and robust ionospheric modeling framework that enhances BDS PPP and PPP-AR performance under varying ionospheric conditions. Full article

(This article belongs to the Special Issue Theory and Method of GNSS Precision Positioning and Its New Application)

► Show Figures

Figure 1

19 pages, 1766 KB

Open AccessArticle

Dual-Contrastive Attribute Embedding for Generalized Zero-Shot Learning

by Qin Li, Yujie Long, Zhiyi Zhang and Kai Jiang

Electronics 2025, 14(21), 4341; https://doi.org/10.3390/electronics14214341 - 5 Nov 2025

Viewed by 320

Abstract

Zero-shot learning (ZSL) aims to categorize target classes with the aid of semantic knowledge and samples from previously seen classes. In this process, the alignment of visual and attribute modality features is key to successful knowledge transfer. Several previous studies have investigated the [...] Read more.

Zero-shot learning (ZSL) aims to categorize target classes with the aid of semantic knowledge and samples from previously seen classes. In this process, the alignment of visual and attribute modality features is key to successful knowledge transfer. Several previous studies have investigated the extraction of attribute-related local features to reduce visual-semantic domain gaps and overcome issues with domain shifts. However, these techniques do not emphasize the commonality of features across different objects belonging to the same attribute, which is critical for identifying and distinguishing the attributes of unseen classes. In this study, we propose a novel ZSL method, termed dual-contrastive attribute embedding (DCAE), for generalized zero-shot learning. This approach simultaneously learns both class-level and attribute-level prototypes and representations. Specifically, an attribute embedding module is introduced to capture attribute-level features and an attribute semantic encoder is developed to generate attribute prototypes. Attribute-level and class-level contrastive loss terms are then used to optimize an attribute embedding space such that attribute features are compactly distributed around corresponding prototypes. This double contrastive learning mechanism facilitates the alignment of multimodal information from two dimensions. Extensive experiments with three benchmark datasets demonstrated the superiority of the proposed method compared to current state-of-the-art techniques. Full article

► Show Figures

Figure 1

19 pages, 1906 KB

Open AccessArticle

Robust OTFS-ISAC for Vehicular-to-Base Station End-to-End Sensing and Communication

by Khurshid Hussain, Esraa Musa Ali, Waeed Hussain, Ali Raza and Dalia H. Elkamchouchi

Electronics 2025, 14(21), 4340; https://doi.org/10.3390/electronics14214340 - 5 Nov 2025

Cited by 1 | Viewed by 675

Abstract

This paper presents an orthogonal time–frequency space (OTFS)-based integrated sensing and communication (ISAC) framework for vehicular-to-base-station (V2B) scenarios, where a synthetic road environment models vehicular mobility and multipath propagation with explicit ground truth. In the sensing stage, OTFS probing signals with Gray-coded quadrature [...] Read more.

This paper presents an orthogonal time–frequency space (OTFS)-based integrated sensing and communication (ISAC) framework for vehicular-to-base-station (V2B) scenarios, where a synthetic road environment models vehicular mobility and multipath propagation with explicit ground truth. In the sensing stage, OTFS probing signals with Gray-coded quadrature amplitude modulation (QAM) are processed via inverse symplectic finite Fourier transform (ISFFT) and cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM). The receiver applies cyclic prefix (CP) removal, fast Fourier transform (FFT), and symplectic finite Fourier transform (SFFT) to extract delay–Doppler (DD) responses. Channel estimation uses time–frequency least squares (TF-LS), robust background suppression, constant false alarm rate (CFAR) detection, and non-maximum suppression (NMS), yielding Precision = 0.79, Recall = 0.84, and F1 = 0.82. Communication decoding employs per-bin least squares, minimum mean-squared error (MMSE) equalization, and Gray-mapped QAM demapping. Across ten frames at 20 dB SNR, the system decoded

1.887 \times 10^{8}

bits with

1.575 \times 10^{5}

errors, producing a bit error rate (BER) of

8.34 \times 10^{- 4}

. Error vector magnitude (EVM) analysis reports mean = 0.30%, median = 0.06%, confirming constellation stability. Random Forest (RF) and imbalanced RF (IRF) classifiers trained on augmented DD payloads achieve Precision = 0.94, Recall = 0.87, and F1 = 0.92. Results validate OTFS-ISAC as a robust framework for V2B communication and sensing. Full article

(This article belongs to the Special Issue Integrated Sensing and Communications for 6G)

► Show Figures

Figure 1

29 pages, 1003 KB

Open AccessArticle

A Secure and Efficient KA-PRE Scheme for Data Transmission in Remote Data Management Environments

by JaeJeong Shin, Deok Gyu Lee, Daehee Seo, Wonbin Kim and Su-Hyun Kim

Electronics 2025, 14(21), 4339; https://doi.org/10.3390/electronics14214339 - 5 Nov 2025

Viewed by 407

Abstract

In recent years, remote data management environments have been increasingly deployed across diverse infrastructures, accompanied by a rapid surge in the demand for sharing and collaborative processing of sensitive data. Consequently, ensuring data security and privacy protection remains a fundamental challenge. A representative [...] Read more.

In recent years, remote data management environments have been increasingly deployed across diverse infrastructures, accompanied by a rapid surge in the demand for sharing and collaborative processing of sensitive data. Consequently, ensuring data security and privacy protection remains a fundamental challenge. A representative example of such an environment is the cloud, where efficient mechanisms for secure data sharing and access control are essential. In domains such as finance, healthcare, and public administration, where large volumes of sensitive information are processed by multiple participants, traditional access-control techniques often fail to satisfy the stringent security requirements. To address these limitations, Key-Aggregate Proxy Re-Encryption (KA-PRE) has emerged as a promising cryptographic primitive that simultaneously provides efficient key management and flexible authorization. However, existing KA-PRE constructions still suffer from several inherent security weaknesses, including aggregate-key leakage, ciphertext insertion and regeneration attacks, metadata exposure, and the lack of participant anonymity within the data-management framework. To overcome these limitations, this study systematically analyzes potential attack models in the KA-PRE setting and introduces a novel KA-PRE scheme designed to mitigate the identified vulnerabilities. Furthermore, through theoretical comparison with existing approaches and an evaluation of computational efficiency, the proposed scheme is shown to enhance security while maintaining practical performance and scalability. Full article

(This article belongs to the Special Issue Wireless Artificial Intelligent Computing Systems and Applications (WASA))

► Show Figures

Figure 1

21 pages, 1995 KB

Open AccessArticle

A Feasibility Study on Enhanced Mobility and Comfort: Wheelchairs Empowered by SSVEP BCI for Instant Noise Cancellation and Signal Processing in Assistive Technology

by Chih-Tsung Chang, Kai-Jun Pai, Ming-An Chung and Chia-Wei Lin

Electronics 2025, 14(21), 4338; https://doi.org/10.3390/electronics14214338 - 5 Nov 2025

Viewed by 386

Abstract

Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) technology offers a promising solution for wheelchair control by translating neural signals into navigation commands. A major challenge—signal noise caused by eye blinks—is addressed in this feasibility study through real-time blink detection and correction. The [...] Read more.

Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) technology offers a promising solution for wheelchair control by translating neural signals into navigation commands. A major challenge—signal noise caused by eye blinks—is addressed in this feasibility study through real-time blink detection and correction. The proposed design utilizes sensors to capture both SSVEP and blink signals, enabling the isolation and compensation of interference, which improves control accuracy by 14.68%. Real-time correction during blinks significantly enhances system reliability and responsiveness. Furthermore, user data and global positioning system (GPS) trajectories are uploaded to the cloud via Wi-Fi 6E for continuous safety monitoring. This approach not only restores mobility for users with physical disabilities but also promotes independence and spatial autonomy. Full article

(This article belongs to the Special Issue Innovative Designs in Human–Computer Interaction)

► Show Figures

Figure 1

37 pages, 3496 KB

Open AccessArticle

A-WHO: Stagnation-Based Adaptive Metaheuristic for Cloud Task Scheduling Resilient to DDoS Attacks

by Fatih Kaplan and Ahmet Babalık

Electronics 2025, 14(21), 4337; https://doi.org/10.3390/electronics14214337 - 5 Nov 2025

Viewed by 303

Abstract

Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a [...] Read more.

Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a stagnation detection mechanism and a stagnation-driven adaptive leader perturbation strategy. The proposed mechanism dynamically applies a noise-guided perturbation into the stallion position only when no improvement is observed over a predefined threshold, enabling A-WHO to escape local optima without modifying the standard behavior of WHO in normal iterations. In addition, a DDoS-aware CloudSim environment is developed by generating attacker virtual machines and high-MI malicious cloudlets to emulate realistic resource exhaustion scenarios. A-WHO’s performance is assessed using makespan, SLA violation rate, each of the QoS metrics, and energy consumption on normal and DDoS conditions. The experimental results indicate that A-WHO achieves the best absolute makespan and QoS metrics during an attack and competitive results under normal conditions. In comparison with the WHO, PSO, ABC, GA, SCA, and CSOA, the proposed approach demonstrates improved robustness and greater resilience to resource degradation attacks. These findings indicate that integrating stagnation-aware diversification into metaheuristic schedulers represents a promising direction for securing cloud task scheduling frameworks. Full article

(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence, 2nd Edition)

► Show Figures

Figure 1

21 pages, 669 KB

Open AccessArticle

Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data

by Takuya Nakata, Jiro Hashizume, Akihiro Yanada and Masahide Nakamura

Electronics 2025, 14(21), 4336; https://doi.org/10.3390/electronics14214336 - 5 Nov 2025

Viewed by 268

Abstract

This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without [...] Read more.

This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without direct shower labels. The proxy feature (washing_seconds) serves as an indirect descriptor of washing behavior, enabling effective inference even under label scarcity. We investigated three research questions: (RQ1) the effectiveness of proxy features in improving shower detection, (RQ2) how proxy-driven evaluation identifies compact yet reliable feature subsets, and (RQ3) the robustness of these subsets in long-term, real-world scenarios. Experiments on two households showed that washing_seconds consistently improved discrimination (raising summer PR-AUC, lowering non-summer false alarms), and that compact subsets of only two or three features, anchored by the proxy feature, achieved stable performance across households. The evaluation represents an illustrative example based on two cooperating households, providing practical evidence of the framework’s real-world applicability. Evaluation in real-world conditions confirmed robustness: representative subsets maintained micro PR-AUC 0.724–0.728, micro F1 0.66–0.69 (macro F1 0.55–0.58), and summer PR-AUC near 0.87, with generalization gaps within ±0.01 for discrimination and small positive shifts for F1 (+0.02–+0.05). These results demonstrate that proxy can function both as a feature and as a methodological principle, and that the proposed framework is model-agnostic and transferable to other learning architectures. It provides a foundation for adaptive, privacy-preserving smart home applications that can scale to broader household and healthcare contexts. Full article

(This article belongs to the Special Issue Smart Pervasive Technologies Utilizing Non-Verbal Information)

► Show Figures

Figure 1

24 pages, 3200 KB

Open AccessArticle

Enhancing Boundary Precision and Long-Range Dependency Modeling in Medical Imaging via Unified Attention Framework

by Yi Zhu, Yawen Zhu, Hongtao Ma, Bin Li, Luyao Xiao, Xiaxu Wu and Manzhou Li

Electronics 2025, 14(21), 4335; https://doi.org/10.3390/electronics14214335 - 5 Nov 2025

Viewed by 566

Abstract

This study addresses the common challenges in medical image segmentation and recognition, including boundary ambiguity, scale variation, and the difficulty of modeling long-range dependencies, by proposing a unified framework based on a hierarchical attention mechanism. The framework consists of a local detail attention [...] Read more.

This study addresses the common challenges in medical image segmentation and recognition, including boundary ambiguity, scale variation, and the difficulty of modeling long-range dependencies, by proposing a unified framework based on a hierarchical attention mechanism. The framework consists of a local detail attention module, a global context attention module, and a cross-scale consistency constraint module, which collectively enable adaptive weighting and collaborative optimization across different feature levels, thereby achieving a balance between detail preservation and global modeling. The framework was systematically validated on multiple public datasets, and the results demonstrated that the proposed method achieved Dice, IoU, Precision, Recall, and F1 scores of 0.886, 0.781, 0.898, 0.875, and 0.886, respectively, on the combined dataset, outperforming traditional models such as U-Net, Mask R-CNN, DeepLabV3+, SegNet, and TransUNet. On the BraTS dataset, the proposed method achieved a Dice score of 0.922, Precision of 0.930, and Recall of 0.915, exhibiting superior boundary modeling capability in complex brain MRI images. On the LIDC-IDRI dataset, the Dice score and Recall were improved from 0.751 and 0.732 to 0.822 and 0.807, respectively, effectively reducing the missed detection rate of small nodules compared to traditional convolutional models. On the ISIC dermoscopy dataset, the proposed framework achieved a Dice score of 0.914 and a Precision of 0.922, significantly improving the accuracy of skin lesion recognition. The ablation study further revealed that local detail attention significantly enhanced boundary and texture modeling, global context attention strengthened long-range dependency capture, and cross-scale consistency constraints ensured the stability and coherence of prediction results. From a medical economics perspective, the proposed framework has the potential to reduce diagnostic costs and improve healthcare efficiency by enabling faster and more accurate image-based clinical decision-making. In summary, the hierarchical attention mechanism presented in this work not only provides an innovative breakthrough in mathematical modeling but also demonstrates outstanding performance and generalization ability in experiments, offering new perspectives and technical pathways for intelligent segmentation and recognition in medical imaging. Full article

(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)

► Show Figures

Figure 1

17 pages, 10904 KB

Open AccessArticle

Self-Supervised Infrared Image Denoising via Adaptive Gradient-Perception Network for FPN Suppression

by Yue Tang, Chaobo Min, Runzhe Miao and Jiajia Lu

Electronics 2025, 14(21), 4334; https://doi.org/10.3390/electronics14214334 - 5 Nov 2025

Viewed by 436

Abstract

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined [...] Read more.

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined composite loss function, specifically designed for hybrid noise removal in raw infrared images. Our method employs a residual network backbone integrated with an adaptive weighting mechanism and edge-preserving loss, enabling joint modeling of multiple noise types while safeguarding structural edges. Unlike reference-based CNN denoising methods requiring clean images, our solution leverages intrinsic gradient variations within image sequences for adaptive smoothing, eliminating dependency on ground-truth data during training. Rigorous experiments conducted on three public datasets have demonstrated the optimal or suboptimal performance of our method in mixed noise suppression and detail preservation (PSNR > 32.13/SSIM > 0.8363). Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

► Show Figures

Figure 1

18 pages, 2241 KB

Open AccessArticle

FADP-GT: A Frequency-Adaptive and Dual-Pooling Graph Transformer Model for Device Placement in Model Parallelism

by Hao Shu, Wangli Hao, Meng Han and Fuzhong Li

Electronics 2025, 14(21), 4333; https://doi.org/10.3390/electronics14214333 - 5 Nov 2025

Viewed by 265

Abstract

The increasing scale and complexity of graph-structured data necessitate efficient parallel training strategies for graph neural networks (GNNs). The effectiveness of these strategies hinges on the quality of graph feature representation. To this end, we propose a Frequency-Adaptive Dual-Pooling Graph Transformer (FADP-GT) model [...] Read more.

The increasing scale and complexity of graph-structured data necessitate efficient parallel training strategies for graph neural networks (GNNs). The effectiveness of these strategies hinges on the quality of graph feature representation. To this end, we propose a Frequency-Adaptive Dual-Pooling Graph Transformer (FADP-GT) model to enhance feature learning for computational graphs. We propose a Frequency-Adaptive Dual-Pooling Graph Transformer (FADP-GT) model, which incorporates two modules: a Frequency-Adaptive Graph Attention (FAGA) module and a Dual-Pooling Feature Refinement (DPFR) module. The FAGA module adaptively filters frequency components in the spectral domain to dynamically adjust the contribution of high- and low-frequency information in attention computation, thereby enhancing the model’s ability to capture structural information and mitigating the over-smoothing problem in multi-layer network propagation. On the other hand, the DPFR module refines graph features through dual-pooling operations—Global Average Pooling (GAP) and Global Max Pooling (GMP)—along the node dimension, which captures both global feature distributions and salient local patterns to enrich multi-scale representations. By improving graph feature representation, our FADP-GT model indirectly supports the development of efficient device placement strategies, as enhanced feature extraction enables the more accurate modeling of node dependencies in computational graphs. The experimental results demonstrate that FADP-GT outperforms existing methods, reducing the average computation time for device placement by 96.52% and the execution time by 9.06% to 26.48%. Full article

► Show Figures

Figure 1

12 pages, 809 KB

Open AccessArticle

Investigation on Electromagnetic Immunity of Unmanned Aerial Vehicles in Electromagnetic Environment

by Roman Kubacki, Rafał Przesmycki, Marek Bugaj and Dariusz Laskowski

Electronics 2025, 14(21), 4332; https://doi.org/10.3390/electronics14214332 - 5 Nov 2025

Viewed by 692

Abstract

The increasing complexity of the electromagnetic environment poses an increasing risk to unmanned aerial vehicles (UAVs) operating in airspaces subject to adverse electromagnetic effects. This paper investigates the potential electromagnetic interference that UAVs may encounter during flight through the lens of electromagnetic compatibility [...] Read more.

The increasing complexity of the electromagnetic environment poses an increasing risk to unmanned aerial vehicles (UAVs) operating in airspaces subject to adverse electromagnetic effects. This paper investigates the potential electromagnetic interference that UAVs may encounter during flight through the lens of electromagnetic compatibility (EMC), which defines the requirements for the proper operation of UAV electronics. According to existing EMC standards, the immunity threshold for typical commercial drones is 10 V/m. However, European standards for public exposure permit electromagnetic fields and suggest that it is possible for an electromagnetic field of a mobile base station antenna to be as strong as 61 V/m. To assess drone vulnerability to its electromagnetic environment, investigation was conducted in an anechoic chamber, which determined that commercially available drones typically experience uncontrolled descent when subjected to an electric field strength of 30 V/m or higher. The primary coupling path for this interference is through the UAV’s internal cables, as induced parasitic currents perturb the motor control signals. This disruption leads to flight instability as the propellers can no longer be reliably controlled, resulting in flight instabilities. Based on a maximum effective radiated power (ERP) of 40 dBW per sector for a base station antenna, a minimum safe operating distance of 20 m was calculated. Adherence to this safe distance is therefore strongly recommended for any commercial drone operator to avoid EMI-induced flight failure. Full article

(This article belongs to the Special Issue Unmanned Vehicles Systems Application)

► Show Figures

Figure 1

21 pages, 388 KB

Open AccessArticle

PhishGraph: A Disk-Aware Approximate Nearest Neighbor Index for Billion-Scale Semantic URL Search

by Dimitrios Karapiperis, Georgios Feretzakis and Sarandis Mitropoulos

Electronics 2025, 14(21), 4331; https://doi.org/10.3390/electronics14214331 - 5 Nov 2025

Viewed by 634

Abstract

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional [...] Read more.

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional in-memory ANN indexes are prohibitively expensive at this scale, while existing disk-based solutions fail to address the unique challenges of the cybersecurity domain: the high velocity of streaming data, the complexity of hybrid queries involving rich metadata, and the highly skewed, adversarial nature of query workloads. PhishGraph addresses these challenges through a synergistic architecture built upon the foundational principles of DiskANN. Its core is a Vamana proximity graph optimized for SSD residency, but it extends this with three key innovations: a Hybrid Fusion Distance metric that natively integrates structured attributes into the graph’s topology for efficient constrained search; a dual-mode update mechanism that combines high-throughput batch consolidation with low-latency in-place updates for streaming data; and an adaptive maintenance policy that monitors query patterns and dynamically reconfigures graph hotspots to mitigate performance degradation from skewed workloads. Our comprehensive experimental evaluation on a billion-point dataset demonstrates that PhishGraph’s adaptive, hybrid design significantly outperforms strong baselines, offering a robust, scalable, and efficient solution for modern threat intelligence. Full article

(This article belongs to the Special Issue Advanced Research in Technology and Information Systems, 2nd Edition)

► Show Figures

Figure 1

19 pages, 679 KB

Open AccessArticle

Adaptive Service Migration for Satellite Edge Computing via Deep Reinforcement Learning

by Lu Zhao, Lulu Guo, Siyi Ni, Wanqi Qian, Kaixiang Lu, Yong Xie and Jian Zhou

Electronics 2025, 14(21), 4330; https://doi.org/10.3390/electronics14214330 - 5 Nov 2025

Viewed by 526

Abstract

In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, [...] Read more.

In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, which includes both interruption cost and processing cost. To address this problem, we propose ASM-DRL, a deep reinforcement learning approach based on the soft Actor-Critic framework. ASM-DRL introduces an adaptive entropy adjustment mechanism to dynamically balance exploration and exploitation, and adopts a dual-Critic architecture with soft target updates to enhance training stability and reduce Q-value overestimation. Extensive simulations show that ASM-DRL significantly outperforms baseline approaches in reducing service cost. Full article

(This article belongs to the Special Issue Intelligent Cloud–Edge Computing Continuum for Industry 4.0)

► Show Figures

Figure 1

18 pages, 23402 KB

Open AccessArticle

Reliable Backscatter Communication for Distributed PV Systems: Practical Model and Experimental Validation

by Xu Liu, Wu Dong, Xiaomeng He, Wei Tang, Kang Liu, Binyang Yan, Zhongye Cao, Da Chen and Wei Wang

Electronics 2025, 14(21), 4329; https://doi.org/10.3390/electronics14214329 - 5 Nov 2025

Viewed by 391

Abstract

Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly [...] Read more.

Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly neglected in idealized analyses, including uncertain hardware insertion loss, non-ideal antenna gain, spatially varying path loss exponents, and fluctuating noise floors. In this work, we develop a practical model for reliable backscatter communications that explicitly incorporates these impairing factors, and we complement the theoretical development with empirical characterization of each contributing term. To validate the model, we implement a frequency-shift keying (FSK)-based backscatter system employing a non-coherent demodulation scheme with adaptive bit-rate matching, and we conduct comprehensive experiments to evaluate communication range and sensitivity to system parameters. Experimental results demonstrate strong agreement with theoretical predictions: the prototype tag consumes 825 µW in measured operation, and an integrated circuit (IC) implementation reduces consumption to 97.8 µW, while measured communication performance corroborates the model’s accuracy under realistic deployment conditions. Full article

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

► Show Figures

Figure 1

42 pages, 4082 KB

Open AccessArticle

Hybrid Ensemble Deep Learning Framework with Snake and EVO Optimization for Multiclass Classification of Alzheimer’s Disease Using MRI Neuroimaging

by Arej Masod Rajab Alhagi and Oğuz Ata

Electronics 2025, 14(21), 4328; https://doi.org/10.3390/electronics14214328 - 5 Nov 2025

Viewed by 564

Abstract

An early and precise diagnosis is essential for successful intervention in Alzheimer’s disease (AD), a progressive neurological illness. In this study, we present a deep learning-based framework for multiclass classification of AD severity levels using MRI neuroimaging data. The framework integrates multiple convolutional [...] Read more.

An early and precise diagnosis is essential for successful intervention in Alzheimer’s disease (AD), a progressive neurological illness. In this study, we present a deep learning-based framework for multiclass classification of AD severity levels using MRI neuroimaging data. The framework integrates multiple convolutional and transformer-based architectures with a novel hybrid hyperparameter optimization strategy; Snake+EVO surpasses conventional optimizers like Genetic Algorithms and Particle Swarm Optimization by skillfully striking a balance between exploration and exploitation. A private clinical dataset yielded a classification accuracy of 99.81%for the optimized CNN model, while maintaining competitive performance on benchmark datasets such as OASIS and the Alzheimer’s Disease Multiclass Dataset. Ensemble learning further enhanced robustness by leveraging complementary model strengths, and Grad-CAM visualizations provided interpretable heatmaps highlighting clinically relevant brain regions. These findings confirm that hybrid optimization combined with ensemble learning substantially improves diagnostic accuracy, efficiency, and interpretability, establishing the proposed framework as a promising AI-assisted tool for AD staging. Future work will extend this approach to multimodal neuroimaging and longitudinal modeling to better capture disease progression and support clinical translation. Full article

► Show Figures

Figure 1

32 pages, 3533 KB

Open AccessArticle

RDBAlert: An AI-Driven Automated Tool for Effective Identification of Victims’ Personal Information in Ransomware Data Breaches

by Juan Manuel Tejada-Triviño, Elvira Castillo-Fernández, Pedro García-Teodoro and José Antonio Gómez-Hernández

Electronics 2025, 14(21), 4327; https://doi.org/10.3390/electronics14214327 - 4 Nov 2025

Viewed by 773

Abstract

Ransomware attacks are increasingly resulting in the public leakage of sensitive personal data, affecting both individuals and organizations worldwide. Aimed to inform victims when their personal information is compromised, this paper introduces RDBAlert, a rapid and efficient practical tool that automates the [...] Read more.

Ransomware attacks are increasingly resulting in the public leakage of sensitive personal data, affecting both individuals and organizations worldwide. Aimed to inform victims when their personal information is compromised, this paper introduces RDBAlert, a rapid and efficient practical tool that automates the extraction of multimodal personal data from ransomware leak repositories, enabling victims to mitigate damage early and take necessary precautions to protect themselves from further harm. The comprehensive and modular nature of this novel tool contributes several notable features: (i) automation of ransomware data leak detection; (ii) analysis of information in multiple formats and languages by integrating well-known OCR, text/PDF, and image recognition, as well as multimodal currently available AI-related tools; (iii) user-friendly interface for quick and efficient analysis; and (iv) ability to gather forensic evidence for studying security incidents. In addition to the flexible nature of RDBAlert–as each module can be replaced or upgraded with potentially more effective solutions without impacting the overall service–experimental results show that it is highly effective at identifying personal information, which will contribute to the mitigation of ransomware attack consequences. Full article

(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)

► Show Figures

Figure 1

20 pages, 3299 KB

Open AccessArticle

WIS: A Technology of Wireless Non-Contact Incremental Training Sensing

by Guanjie Wang, Yu Wu, Hongyu Sun, Xinyue Zhang, Wanjia Li and Yanhua Dong

Electronics 2025, 14(21), 4326; https://doi.org/10.3390/electronics14214326 - 4 Nov 2025

Viewed by 345

Abstract

Wireless contactless human activity sensing is a new type of sensing method that uses the propagation characteristics of wireless signals to accurately perceive and understand human behavior. However, facing the huge amount of newly generated data and expanding action categories in the sensing [...] Read more.

Wireless contactless human activity sensing is a new type of sensing method that uses the propagation characteristics of wireless signals to accurately perceive and understand human behavior. However, facing the huge amount of newly generated data and expanding action categories in the sensing process, the traditional model needs to be retrained frequently. This model not only brings significant computational power overhead, but also seriously affects the real-time response speed of the system. To address this problem, this paper proposes a model, WIS (Wireless Incremental Sense), which is composed of two parts. The first part is the basic sensing module NFFCN (Nearest Neighbor Feature Fusion Classification). NFFCN is a fusion classification method based on Nearest Class Mean (NCM) classifier and Random Forest (RF). By combining the local feature extraction ability of NCM and the integrated learning advantage of RF, this method can efficiently extract human behavior features from wireless signals and achieve high-precision classification. The second part is the incremental learning module NFFCN-RTST, which uses the retraining subtree (RTST) incremental strategy to optimize the model. Unlike update leaf statistics (ULS) and the Incrementally Grow Tree (IGT) incremental strategy, RTST not only updates the statistical data of leaf nodes but also dynamically adjusts the previously learned splitting function, so as to better adapt to new data and categories. In the experimental validation on the rRuler and Stan WiFi datasets, the average recognition accuracy of NFFCN reaches 87.1% and 98.4%, respectively. In the class-incremental experimental validation, the recognition accuracy of WIS reaches 87% and 95%, respectively. Full article

► Show Figures

Figure 1

18 pages, 405 KB

Open AccessArticle

An Efficient Ciphertext-Policy Decryptable Attribute-Based Keyword Search Scheme with Dynamic Attribute Support

by Koon-Ming Chan, Swee-Huay Heng, Syh-Yuan Tan and Shing-Chiang Tan

Electronics 2025, 14(21), 4325; https://doi.org/10.3390/electronics14214325 - 4 Nov 2025

Viewed by 269

Abstract

Safeguarding data confidentiality and enforcing precise access regulation in cloud platforms continue to be major research concerns. Attribute-based encryption (ABE) offers a versatile framework for policy-driven control, whereas public key encryption with keyword search (PEKS) supports efficient querying of encrypted datasets. However, ABE [...] Read more.

Safeguarding data confidentiality and enforcing precise access regulation in cloud platforms continue to be major research concerns. Attribute-based encryption (ABE) offers a versatile framework for policy-driven control, whereas public key encryption with keyword search (PEKS) supports efficient querying of encrypted datasets. However, ABE lacks keyword search support, and PEKS offers limited control over access policies. To overcome these limitations, attribute-based keyword search (ABKS) schemes have been proposed, with recent advances such as ciphertext-policy decryptable ABKS (CP-DABKS) enabling secure channel-free keyword search. Nevertheless, the existing CP-DABKS schemes still face important challenges: the master public key grows linearly with the attribute universe, secure channels are often required to deliver trapdoors, and many designs remain vulnerable to keyword guessing attacks. This work introduces an efficient CP-DABKS scheme built upon a Type-3 pairing framework to directly overcome these limitations. The proposed design employs a commit-to-point mechanism that prevents linear key growth, eliminates the need for secure trapdoor transmission, and resists keyword guessing attacks. We implement and evaluate the proposed scheme using real-world data from the Enron Email dataset and demonstrate its practicality for secure and searchable cloud-based storage. We also discuss implementation considerations and outline directions for future enhancement of privacy-preserving searchable encryption systems. Full article

(This article belongs to the Special Issue Future Innovations in Cryptography for Authentication and Data Security)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Electronics, Volume 14, Issue 21 (November-1 2025) – 222 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI