Electronics

37 pages, 1143 KiB

Open AccessReview

Cyber–Physical Resilience: Evolution of Concept, Indicators, and Legal Frameworks

by Antonella Longo, Ali Aghazadeh Ardebili, Alessandro Lazari and Antonio Ficarella

Electronics 2025, 14(8), 1684; https://doi.org/10.3390/electronics14081684 (registering DOI) - 21 Apr 2025

The protection of critical infrastructures (CIs) from cyber–physical threats and natural hazards has become increasingly vital in modern society, which relies heavily on the essential services provided by these infrastructures. The European Union has emphasized the importance of this issue by deploying a [...] Read more.

The protection of critical infrastructures (CIs) from cyber–physical threats and natural hazards has become increasingly vital in modern society, which relies heavily on the essential services provided by these infrastructures. The European Union has emphasized the importance of this issue by deploying a comprehensive policy package in 2022, including the NIS2 and CER Directives. This paper explores the concept of resilience in critical entities and essential services from a cyber–physical perspective. It addresses the inherent complexity of CIs and discusses challenges, limitations, and future research directions for enhancing their protection in line with EU policies. Furthermore, it introduces a conceptual model of resilience, outlining its analytical dimensions, and reviews current resilience indicators and corresponding assessment frameworks. Full article

(This article belongs to the Special Issue Cyber-Physical Systems: Recent Developments and Emerging Trends)

17 pages, 18396 KiB

Open AccessArticle

SSA-VMD-Double-Fuzzy-Logic for Human Vital Signs Detection Using a UWB Radar

by Ji Li, Weixin Zhang, Zeping Xu, Yunpeng Wang, Zhaotian Deng, Chengwu You and Chengpei Tang

Electronics 2025, 14(8), 1683; https://doi.org/10.3390/electronics14081683 (registering DOI) - 21 Apr 2025

Abstract

Ultra-wide-band (UWB) radar technology is a contactless signal detection technology. The UWB technology can be used for the clinical medical monitoring of the human heartbeat and respiration. In this paper, a novel algorithm is proposed to estimate heart rate (HR) and respiratory rate [...] Read more.

Ultra-wide-band (UWB) radar technology is a contactless signal detection technology. The UWB technology can be used for the clinical medical monitoring of the human heartbeat and respiration. In this paper, a novel algorithm is proposed to estimate heart rate (HR) and respiratory rate (RR) depending on the received UWB radar signal. The proposed algorithm initially applies Singular Spectrum Analysis (SSA) to remove noise from the received UWB radar signal, and then applies Variational Mode Decomposition (VMD) to effectively separate the respiratory and heartbeat components. To address the issue of heartbeat components being susceptible to harmonic and inter-modulation components interference, a double fuzzy logic estimation is implemented to achieve robust real-time extraction of heart rate. In this paper, extensive experiments are conducted at various distances and angles. The experimental results of the SSA-VMD-Double-Fuzzy-Logic (SVDF) method have been compared with other methods, demonstrating its effectiveness and the advantages of the SVDF proposed in this paper. Full article

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19 pages, 2300 KiB

Open AccessArticle

Beyond Isolated Features: Group-Level Feature-Driven Multimodal Fusion for Entity Relationship Extraction

by Yana Lv, Jiaqi Tao and Xiuli Du

Electronics 2025, 14(8), 1682; https://doi.org/10.3390/electronics14081682 (registering DOI) - 21 Apr 2025

Abstract

Named entity recognition and relation extraction are two crucial techniques in the construction of knowledge graphs, as their performance directly impacts downstream tasks. In scenarios such as social media, where text is short and contains numerous references, relying solely on a single modality [...] Read more.

Named entity recognition and relation extraction are two crucial techniques in the construction of knowledge graphs, as their performance directly impacts downstream tasks. In scenarios such as social media, where text is short and contains numerous references, relying solely on a single modality often leads to suboptimal entity-relation extraction results. To address this issue, this paper proposes a multimodal entity-relation extraction model. The model incorporates group-level features into visual representations and integrates them into a BERT variant using a dynamic gating mechanism and an attention mechanism. This approach effectively reduces modal noise, balances the gap between modalities to some extent, and enhances both encoding performance and data utilization. The experimental results demonstrate that the proposed model achieves F1 scores of 87.78% and 84.16% on public datasets for MNER and MRE tasks, respectively, outperforming baseline models. Additionally, ablation studies validate the effectiveness of each module within the proposed model. Full article

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11 pages, 921 KiB

Open AccessArticle

A Physiological Evaluation of Driver Workload in the Lead Vehicle of an Autonomous Truck Platoon Using Bio-Signal Analysis

by Emi Yuda, Junichiro Hayano and Makoto Takahashi

Electronics 2025, 14(8), 1681; https://doi.org/10.3390/electronics14081681 (registering DOI) - 21 Apr 2025

Abstract

The evaluation of driver workload in the lead vehicle of a driver-following autonomous truck platoon was conducted using bio-signal analysis. In this study, a single driver operated the lead vehicle while the second and third trucks followed autonomously. Three professional truck drivers (38 [...] Read more.

The evaluation of driver workload in the lead vehicle of a driver-following autonomous truck platoon was conducted using bio-signal analysis. In this study, a single driver operated the lead vehicle while the second and third trucks followed autonomously. Three professional truck drivers (38 ± 4 years old, male) participated in the experiment. During driving, wearable sensors measured heart-rate variability indices, body acceleration, and skin temperature. The heart rate and body acceleration were sampled at 128 Hz (7.8 ms intervals), while skin temperature was recorded at 1 Hz. Each participant underwent three measurement sessions on different days, with each session lasting approximately 30–40 min. Statistical analysis was performed using repeated-measures ANOVA to determine significant differences across conditions and days. The results indicated that compared to solo driving, driving the lead vehicle of the autonomous platoon significantly increased skin temperature (p < 0.001), suggesting a higher physiological workload. This study provides insight into the physiological impact of autonomous platooning on lead-vehicle drivers, which is crucial for developing strategies to mitigate driver workload in such systems. Full article

(This article belongs to the Special Issue New Application of Wearable Electronics)

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25 pages, 3761 KiB

Open AccessArticle

RBFAC: A Redactable Blockchain Framework with Fine-Grained Access Control Based on Flexible Policy Chameleon Hash

by Shiyang Wu, Lifei Wei, Shihai Wu and Lei Zhang

Electronics 2025, 14(8), 1680; https://doi.org/10.3390/electronics14081680 (registering DOI) - 21 Apr 2025

Abstract

While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, [...] Read more.

While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, current redactable blockchain implementations exhibit significant limitations, particularly in their inability to separate data editing from policy modification and their insufficient support for decentralized management of diverse editing operations. To address these issues, this paper initially introduces the concept of Flexible Policy Chameleon Hash (FPCH), which integrates PCH with non-interactive zero-knowledge proofs to enable enhanced policy management flexibility. Moreover, this paper proposes a Redactable Blockchain Framework with Fine-grained Access Control (RBFAC) based on FPCH. The RBFAC framework employs a hybrid cryptographic approach to separate the right of data editing from policy modification. The framework also provides essential functionalities, including editing accountability, key tracking and revocation mechanisms, and policy privacy protection. Finally, experimental evaluations demonstrate that the RBFAC framework maintains acceptable performance overhead while delivering these advanced features. The results indicate that the proposed solution addresses the limitations of existing redactable blockchain systems, offering a more flexible and secure approach to controlled data editing in blockchain environments. Full article

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

25 pages, 3995 KiB

Open AccessArticle

CNN-Based Fault Classification in Induction Motors Using Feature Vector Images of Symmetrical Components

by Tae-Hong Min, Joong-Hyeok Lee and Byeong-Keun Choi

Electronics 2025, 14(8), 1679; https://doi.org/10.3390/electronics14081679 - 21 Apr 2025

Abstract

Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on [...] Read more.

Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article

(This article belongs to the Special Issue AI in Signal and Image Processing)

23 pages, 2111 KiB

Open AccessArticle

GAShellBreaker: A Novel Method for Java Fileless Webshell Detection Based on Grayscale Images and Deep Learning

by Yuan Zhang, Daofeng Li and Yuqin Xie

Electronics 2025, 14(8), 1678; https://doi.org/10.3390/electronics14081678 - 21 Apr 2025

Abstract

Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers are shifting toward fileless Webshells, which reside entirely in memory and present significant challenges to conventional [...] Read more.

Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers are shifting toward fileless Webshells, which reside entirely in memory and present significant challenges to conventional security tools. However, research on fileless Webshell detection remains limited. To address this gap, we analyzed various fileless Webshell samples, summarized their behavioral patterns, and constructed a corresponding threat model. Based on this, we propose a novel detection approach named GAShellBreaker, which leverages grayscale image transformation and deep learning. GAShellBreaker first establishes a dual-layer in-memory monitoring mechanism to capture suspicious classes within the Java Virtual Machine (JVM) and export them as bytecode files. It then extracts opcode sequences from these files, transforms them into grayscale images, and employs a ResNet50-based classifier for detection. Due to the limited availability of fileless samples, we trained and evaluated the model on a larger dataset of 1351 file-based scripts (383 Webshells and 968 benign samples), and used 56 fileless Webshells for validation. Experimental results show that GAShellBreaker achieves 99.10% accuracy on file-based Webshells and 89.29% accuracy on fileless Webshells, outperforming existing algorithms. Moreover, it maintains low computational overhead (6.7%), confirming its practical feasibility. Full article

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7 pages, 177 KiB

Open AccessEditorial

Advanced Research in Technology and Information Systems

by Vasile-Daniel Pavaloaia, Rodrigo Martin-Rojas and Piotr Sulikowski

Electronics 2025, 14(8), 1677; https://doi.org/10.3390/electronics14081677 - 21 Apr 2025

Abstract

The fields of information systems and technology continue to evolve at a rapid pace, offering new research directions and expanding career opportunities [...] Full article

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

39 pages, 3508 KiB

Open AccessArticle

IV-Nlp: A Methodology to Understand the Behavior of DL Models and Its Application from a Causal Approach

by Yudi Guzman-Monteza, Juan M. Fernandez-Luna and Francisco J. Ribadas-Pena

Electronics 2025, 14(8), 1676; https://doi.org/10.3390/electronics14081676 (registering DOI) - 21 Apr 2025

Abstract

Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its application. IV-NLP integrates two approaches. The first defines the [...] Read more.

Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its application. IV-NLP integrates two approaches. The first defines the process of the inference and estimation of the causal effect in original, predicted, and synthetic data. The second one includes a validation method of the results obtained by the selected Large-Language Model (LLM). IV-NLP proposes to use synthetic data in predictive tasks only if the causal effect pattern of the synthetic data is aligned with the causal effect pattern of the original data. DL models, the Instrumental Variable (IV) method, statistical methods, and GPT-3.5-turbo-0125 were used for its application, including an intervention method using a variation of the Retrieval-Augmented Generation (RAG) technique. Our findings reveal notable discrepancies between the original and synthetic data, highlighting that the synthetic data do not fully capture the underlying causal effect patterns of the original data, evidencing homogeneity and low diversity in the synthetic data. Interestingly, when evaluating the causal effect in the predictions made by our three best DL models, it was verified that the model with the lowest accuracy (84.50%) was fully aligned with the overall causal effect pattern. These results demonstrate the potential of integrating DL and LLM models with causal inference methods. Full article

(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)

22 pages, 4505 KiB

Open AccessArticle

Advancing Secret Sharing in 3D Models Through Vertex Index Sharing

by Yuan-Yu Tsai, Jyun-Yu Jhou, Tz-Yi You and Ching-Ta Lu

Electronics 2025, 14(8), 1675; https://doi.org/10.3390/electronics14081675 - 21 Apr 2025

Abstract

Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in [...] Read more.

Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in the realm of images, its exploration and application in 3D models remain in their early stages. Given the growing prominence of 3D models in multimedia applications, ensuring their security and privacy has emerged as a critical area of research. At present, secret sharing approaches for 3D models predominantly rely on the vertex coordinates of the model as the basis for embedding and reconstructing secret messages. However, due to the limited quantity of vertex coordinates, these methods face significant constraints in embedding capacity, thereby limiting the potential of 3D models in secure data sharing. In contrast, the vertex indices of polygons, characterized by higher information density and greater structural flexibility, present a promising alternative medium for embedding secret shares. Building on this premise, the present study investigates the feasibility of leveraging shared vertex indices as a foundation for message embedding. It highlights the advantages of this approach in enhancing both the embedding capacity and the overall security of 3D models. By integrating the Chinese Remainder Theorem into vertex index-based sharing, the proposed method strengthens existing algorithms, offering improved model protection and enhanced embedding security. Experimental evaluations reveal that, compared to traditional vertex coordinate-based methods, incorporating vertex indices into secret sharing techniques significantly increases embedding efficiency while bolstering the security of 3D models. This study not only introduces an innovative approach to safeguarding 3D model data but also paves the way for the broader application of secret sharing techniques in the future. Full article

(This article belongs to the Special Issue Advancements in Network and Data Security)

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19 pages, 1090 KiB

Open AccessArticle

TeeDFuzzer: Fuzzing Trusted Execution Environment

by Sheng Wen, Liam Xu, Liwei Tian, Suping Liu and Yong Ding

Electronics 2025, 14(8), 1674; https://doi.org/10.3390/electronics14081674 - 21 Apr 2025

Abstract

The Trusted Execution Environment (TEE) is crucial for safeguarding the ecosystem of embedded systems. It uses isolation to minimize the TCB (Trusted Computing Base) and protect sensitive software. It is vital because devices handle vast, potentially sensitive data. Leveraging ARM TrustZone, widely used [...] Read more.

The Trusted Execution Environment (TEE) is crucial for safeguarding the ecosystem of embedded systems. It uses isolation to minimize the TCB (Trusted Computing Base) and protect sensitive software. It is vital because devices handle vast, potentially sensitive data. Leveraging ARM TrustZone, widely used in mobile and IoT for TEEs, it ensures hardware protection via security extensions, though needing firmware and software stack support. Despite the reputation of TEEs for high security, TrustZone-aided ones have vulnerabilities. Fuzzing, as a practical bug-finding technique, has seen limited research in the context of TEE. The unique software architecture of TrustZone-assisted TEE complicates the direct application of traditional fuzzing methods. Moreover, simplistic approaches, such as feeding random input values into TEE through the API functions of the rich operating system, fail to uncover deeper, latent bugs within the TEE code. In this paper, we present a fuzzing strategy for TrustZone-assisted TEE that utilizes inferred dependencies between Trusted Kernel system calls to uncover deep-seated TEE bugs. We implemented our approach on OP-TEE, where it successfully identified 17 crashes, including one previously undetected kernel bug. Full article

(This article belongs to the Special Issue Advances in Software Engineering and Programming Languages)

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14 pages, 4120 KiB

Open AccessArticle

SACG-YOLO: A Method of Transmission Line Insulator Defect Detection by Fusing Scene-Aware Information and Detailed-Content-Guided Information

by Lihui Zhao, Jun Kang, Yang An, Yurong Li, Meili Jia and Ruihong Li

Electronics 2025, 14(8), 1673; https://doi.org/10.3390/electronics14081673 - 20 Apr 2025

Abstract

To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module [...] Read more.

To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module is employed to extract global environmental information, improving the baseline model’s adaptability to complex backgrounds. Second, a detailed content attention module is introduced to enable the model to more accurately capture fine-grained features of small defect regions. Furthermore, a normalized Wasserstein distance metric function is adopted to mitigate the sensitivity of the regression branch in the baseline model. Simultaneously, a sample weighting function is utilized to reduce the impact of sample imbalance on the classification branch. Experimental results demonstrate that the proposed method achieves superior detection performance on a real-world transmission line insulator defect dataset. Full article

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23 pages, 9237 KiB

Open AccessArticle

Design and Optimization of an Internet of Things-Based Cloud Platform for Autonomous Agricultural Machinery Using Narrowband Internet of Things and 5G Dual-Channel Communication

by Baidong Zhao, Dingkun Zheng, Chenghan Yang, Shuang Wang, Madina Mansurova, Sholpan Jomartova, Nadezhda Kunicina, Anatolijs Zabasta, Vladimir Beliaev, Jelena Caiko and Roberts Grants

Electronics 2025, 14(8), 1672; https://doi.org/10.3390/electronics14081672 - 20 Apr 2025

Abstract

This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and [...] Read more.

This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations. Full article

(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)

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22 pages, 7958 KiB

Open AccessArticle

Depth Upsampling with Local and Nonlocal Models Using Adaptive Bandwidth

by Niloufar Salehi Dastjerdi and M. Omair Ahmad

Electronics 2025, 14(8), 1671; https://doi.org/10.3390/electronics14081671 - 20 Apr 2025

Abstract

The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. [...] Read more.

The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article

(This article belongs to the Special Issue Image and Video Processing for Emerging Multimedia Technology)

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21 pages, 13056 KiB

Open AccessArticle

Package Integration and System Performance Analysis of Glass-Based Passive Components for 5G New Radio Millimeter-Wave Modules

by Muhammad Ali, Atom Watanabe, Takenori Kakutani, Pulugurtha M. Raj, Rao. R. Tummala and Madhavan Swaminathan

Electronics 2025, 14(8), 1670; https://doi.org/10.3390/electronics14081670 - 20 Apr 2025

Abstract

In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and [...] Read more.

In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and n260 are modeled, designed, fabricated and characterized individually along with their integrated versions. Non–contiguous diplexers are designed using three different types of filters, hairpin, interdigital and edge–coupled, and combined with a broadband coupler to emulate a power detection and control circuitry block in an RF transmitter chain. A panel–compatible semi–additive patterning (SAP) process is utilized to form high–precision redistribution layers (RDLs) on laminated glass substrate, onto which fine features with tight tolerance are added to fabricate these structures. The diplexers exhibit low insertion loss, low VSWR and high isolation, and have a small footprint. A system performance analysis using a co–simulation technique is presented for the first time to quantify the distortion in amplitude and phase produced by the fabricated passive component block in terms of error vector magnitude (EVM). Moreover, the scalability of this approach to compare similar passive components based on their specifications and signatures using a system–level performance metric such as EVM is discussed. Full article

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

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19 pages, 12455 KiB

Open AccessArticle

Research on Mobile Robot Path Planning Based on an Improved Bidirectional Jump Point Search Algorithm

by Rui Guo, Xingbo Quan and Changchun Bao

Electronics 2025, 14(8), 1669; https://doi.org/10.3390/electronics14081669 - 20 Apr 2025

Abstract

This study proposes an improved bidirectional dynamic jump point search (JPS) algorithm to address key challenges in mobile robot path planning, including excessive node expansions, poor path smoothness, safety concerns, and extended search times. The core novelty of this algorithm lies in the [...] Read more.

This study proposes an improved bidirectional dynamic jump point search (JPS) algorithm to address key challenges in mobile robot path planning, including excessive node expansions, poor path smoothness, safety concerns, and extended search times. The core novelty of this algorithm lies in the introduction of adaptive weight coefficients in the heuristic function and dynamic constraint circles to optimize node expansions. Specifically, the adaptive heuristic function dynamically adjusts the weight coefficients based on the current position relative to the target point, significantly accelerating path searches while ensuring accuracy. Additionally, a dynamically constrained circle is introduced, which defines an adaptive search region, prioritizing node expansions within its boundary and effectively reducing unnecessary searches. Moreover, the jump point selection rules have been optimized to eliminate hazardous nodes and further improve path safety and practicality. Simulation tests conducted on grid maps with varying complexities clearly demonstrate that the proposed algorithm considerably reduces search times by up to 66.27% compared with conventional A*, traditional JPS, and bidirectional JPS methods. Finally, physical mobile robot experiments further validate the effectiveness and real-world applicability of the proposed algorithm. Full article

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19 pages, 8428 KiB

Open AccessFeature PaperArticle

Cascadable Complementary SSF-Based Biquads with 8 GHz Cutoff Frequency and Very Low Power Consumption

by Matteo Lombardo, Francesco Centurelli, Pietro Monsurrò and Alessandro Trifiletti

Electronics 2025, 14(8), 1668; https://doi.org/10.3390/electronics14081668 - 20 Apr 2025

Abstract

Low-pass filters with bandwidths larger than several GHz are required in many applications, such as anti-aliasing filters in high-speed ADCs and pulse-shaping filters in high-speed DACs. In highly integrated applications, low area occupation and power consumption are key specifications, so inductor-less implementations are [...] Read more.

Low-pass filters with bandwidths larger than several GHz are required in many applications, such as anti-aliasing filters in high-speed ADCs and pulse-shaping filters in high-speed DACs. In highly integrated applications, low area occupation and power consumption are key specifications, so inductor-less implementations are to be preferred. Furthermore, full CMOS implementations provide an advantage in terms of technology availability and cost. In this paper, we present an inductor-less CMOS biquad stage based on the super source follower topology that provides an 8 GHz cutoff frequency and a low power consumption of 0.42 mW per pole, showing remarkable performance also in terms of bandwidth and dynamic range. The availability of two separate current sources allows independent tuning of natural frequency and quality factor. The stage can be implemented in two complementary ways, exploiting NMOS and PMOS input devices, respectively, thus simplifying cascadability. The two complementary biquads have been implemented in the STMicroelectronics FDSOI 28 nm CMOS process and extensively simulated and provide stable performance under PVT variations and mismatches. The area occupation is about 387.5 μm² per biquad, one of the lowest in the literature. The figures-of-merit are remarkable, as the filters achieve excellent power efficiency, very low area occupation, and good dynamic range. Full article

(This article belongs to the Special Issue Advances in RF, Analog, and Mixed Signal Circuits)

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19 pages, 4401 KiB

Open AccessArticle

An Integrated RF Sensor Design for Anchor-Free Collaborative Localization in GNSS-Denied Environments

by Di Bai, Xinran Li, Lingyun Zhou, Chunyong Yang, Yongqiang Cui, Liyun Bai and Yunhao Chen

Electronics 2025, 14(8), 1667; https://doi.org/10.3390/electronics14081667 - 20 Apr 2025

Abstract

To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor [...] Read more.

To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor estimates inter-node distances and orientations using wireless measurements between nodes, without requiring pre-deployed anchor points. First, we designed a low-nanosecond latency ranging logic circuit on field-programmable gate array (FPGA) hardware, enabling relative distance estimation between nodes via a low-latency collaborative ranging (LLCR) algorithm without synchronization. Additionally, a synthetic aperture rotating antenna system was built to construct an echo space energy distribution matrix, based on dynamic–static dual-channel phase differences for high-precision, unambiguous azimuth measurement, followed by angle and distance data integration for localization. Then, a novel RF sensor hardware system was designed that was lightweight, low in cost, and high in performance. Finally, two generations of prototype models were developed and tested in both an anechoic chamber and mounted on unmanned vehicles outdoors in fields. The results demonstrate that the proposed sensor can achieve high-precision relative position estimation between collaborative nodes in the absence of GNSS, with a positioning error of within 0.4 m, indicating that it is suitable for mounting on unmanned vehicles and other autonomous systems for collaborative positioning. Full article

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

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12 pages, 1258 KiB

Open AccessArticle

Efficient Multiple-Input–Multiple-Output Channel State Information Feedback: A Semantic-Knowledge-Base- Driven Approach

by Ling Tang, Yaping Sun, Shumin Yao, Xiaodong Xu, Hao Chen and Zhiyong Luo

Electronics 2025, 14(8), 1666; https://doi.org/10.3390/electronics14081666 - 20 Apr 2025

Abstract

Massive multiple-input–multiple-output (MIMO) systems encounter substantial challenges in relation to channel state information (CSI) feedback; this is particularly the case in frequency division duplex systems, where the lack of channel reciprocity necessitates high-dimensional CSI transmission, resulting in substantial feedback overheads. Most existing deep [...] Read more.

Massive multiple-input–multiple-output (MIMO) systems encounter substantial challenges in relation to channel state information (CSI) feedback; this is particularly the case in frequency division duplex systems, where the lack of channel reciprocity necessitates high-dimensional CSI transmission, resulting in substantial feedback overheads. Most existing deep learning methods have improved compression efficiency but still suffer from high feedback overheads due to their transmission of entire compressed vectors. To address this, we propose SKBNet, an innovative semantic knowledge base (SKB)-driven framework for efficient MIMO CSI feedback. By sharing the SKB between the transmitter and receiver, SKBNet transmits only index values, significantly reducing feedback overheads while achieving efficient CSI compression and accurate reconstruction. The simulation results demonstrate that SKBNet outperforms many existing methods in normalized mean square error, cosine similarity, and feedback bit-count at various compression ratios. This framework offers a promising solution for low-overhead, high-precision CSI feedback for future semantic communication networks. Full article

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Electronics

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