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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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22 pages, 1744 KB  
Article
Hybrid Long-Range–5G Multi-Sensor Platform for Predictive Maintenance for Ventilation Systems
by Praveen Mohanram and Robert H. Schmitt
Electronics 2025, 14(5), 1055; https://doi.org/10.3390/electronics14051055 - 6 Mar 2025
Cited by 1 | Viewed by 2090
Abstract
In this paper, we present a multi-sensor platform for predictive maintenance featuring hybrid long-range (LoRa) and 5G connectivity. This hybrid approach combines LoRa’s low-power transmission for energy efficiency with 5G’s real-time data capabilities. The hardware platform integrates multiple sensors to monitor machine health [...] Read more.
In this paper, we present a multi-sensor platform for predictive maintenance featuring hybrid long-range (LoRa) and 5G connectivity. This hybrid approach combines LoRa’s low-power transmission for energy efficiency with 5G’s real-time data capabilities. The hardware platform integrates multiple sensors to monitor machine health parameters, with data analyzed on the device using pre-trained AI models to assess the machine’s condition. Inferences are transmitted via LoRa to the operator for maintenance scheduling, while a cloud application tracks and stores sensor data. Periodic sensor data bursts are sent via 5G to update the AI model, which is then delivered back to the platform through over-the-air (OTA) updates. We provide a comprehensive overview of the hardware architecture, along with an in-depth analysis of the data generated by the sensors, and its processing methodology. However, the data analysis and the software for ventilation control and its predictive capabilities are not the focus of this paper and are not presented. Full article
(This article belongs to the Special Issue 5G Mobile Telecommunication Systems and Recent Advances)
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19 pages, 7225 KB  
Article
Utilization of MCU and Real-Time Simulator for Identifying Beatless Control for Six-Step Operation of Three-Phase Inverter
by Yongsu Han
Electronics 2025, 14(5), 1030; https://doi.org/10.3390/electronics14051030 - 5 Mar 2025
Viewed by 839
Abstract
In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent [...] Read more.
In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent accidents and shorten development time. This study presents a validation process using a real-time simulator for the beatless control of six-step operation. Six-step operation, when applied to high-speed drives, has a limitation on the number of samples per electrical rotation, which causes voltage errors. A representative of these voltage error phenomena is the beat phenomenon, resulting in torque ripple at the first harmonic and high current ripple. To mitigate this beat phenomenon, a synchronous PWM method is sometimes used. However, in practical industrial systems, it may not be feasible to synchronously adjust the inverter’s switching frequency with the rotation speed. This study proposes a beatless control method to eliminate the voltage errors caused by the beat phenomenon during six-step operation at a fixed switching frequency. The specific implementation of this control method is explained based on MCU timer register settings. While previous studies have only proposed beatless control methods, this paper goes further by implementing the proposed beatless method using the MCU (TMS320F28335) to generate gating signals and validating the implementation through simulation on a permanent magnet synchronous motor using a real-time simulator (Typhoon HIL). Full article
(This article belongs to the Special Issue Hardware in the Loop, Real-Time Simulation and Digital Control of Power Electronics and Drives, 2nd Edition)
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12 pages, 8620 KB  
Article
Picosecond-Level Synchronization over Optical Free Space Link Using White Rabbit
by Peng Zhang, Dong Hou, Ke Liu, Wenjian Zhou, Minghong Li and Lujun Fang
Electronics 2025, 14(5), 970; https://doi.org/10.3390/electronics14050970 - 28 Feb 2025
Viewed by 2703
Abstract
White Rabbit (WR) time synchronization has an accuracy up to a sub-nanosecond level. However, the current application scenario of WR is limited to wired transmission links. In this paper, we have proposed a time synchronization technique over a free space link using WR. [...] Read more.
White Rabbit (WR) time synchronization has an accuracy up to a sub-nanosecond level. However, the current application scenario of WR is limited to wired transmission links. In this paper, we have proposed a time synchronization technique over a free space link using WR. In the WR-based free space synchronization scheme, we replace the original WDM (Wavelength Division Multiplexing) with single-wavelength transmission to reduce the asymmetry of the path and design a high-power optical transceiver module to improve the transmission power. With the scheme, a free space synchronization experiment with a transmission distance of 50 m is conducted. The experimental results show that the RMS (root mean square) time drift of this free space synchronization system is 20.5 ps over a 24 h period, and the TDEV (Time Deviation) of the time synchronization is 14.3 ps at 1 s and 3.9 ps at 20,000 s. The experiment proves that it will be convenient to complete the free space time synchronization network between clock sites with the proposed technique in the future application of complex environments. Full article
(This article belongs to the Special Issue Applications of MEMS and QCM in Smart Sensor Systems)
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23 pages, 10184 KB  
Review
A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges
by Md Motiur Rahman, Saeka Rahman, Smriti Bhatt and Miad Faezipour
Electronics 2025, 14(5), 974; https://doi.org/10.3390/electronics14050974 - 28 Feb 2025
Cited by 2 | Viewed by 3939
Abstract
Fault and defect detection are critical for ensuring the safety, reliability, and quality of products and infrastructure across various industries. As traditional manual inspection methods face limitations in efficiency and accuracy, advancements in artificial intelligence, particularly image segmentation, have paved the way for [...] Read more.
Fault and defect detection are critical for ensuring the safety, reliability, and quality of products and infrastructure across various industries. As traditional manual inspection methods face limitations in efficiency and accuracy, advancements in artificial intelligence, particularly image segmentation, have paved the way for automated and precise fault detection processes. A significant gap exists in current research regarding the integration and comparative analysis of classical and modern segmentation approaches across diverse application domains. This study addresses this gap by providing a systematic review that bridges traditional segmentation techniques with cutting-edge deep learning methodologies. Unlike previous reviews that focus solely on isolated techniques or specific domains, this paper offers a holistic analysis of methodological innovations, application breadth, and emerging trends. Emphasis is placed on the integration of deep learning models, hybrid approaches, and advancements like attention mechanisms and lightweight architectures. Additionally, the review highlights critical challenges and proposes future research directions aimed at enhancing model scalability, robustness, and adaptability. This systematic review addresses gaps in the field and provides useful insights for academia and industry, making it a key reference in fault detection using image segmentation. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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21 pages, 7796 KB  
Article
Electrical Response of Photovoltaic Power Cells to Cosmic Radiation in the Stratosphere
by Tomasz Aleksander Miś, Dominika Pytlak, Bartosz Kościanek, Korneliusz Szalkowski, Jakub Czerniej, Paulina Kucharczyk, Mikołaj Salamon, Marek Pąśko, Karolina Styrna, Sandra Wąsowska, Michał Gołąb, Paweł Urbański, Hubert Tronowski and Damian Legutko
Electronics 2025, 14(5), 991; https://doi.org/10.3390/electronics14050991 - 28 Feb 2025
Cited by 1 | Viewed by 1085
Abstract
This article describes the CURiE (Composites and photovoltaics Undergoing Radiation Exposure) stratospheric experiment, which was designed and built in 2024 for the BEXUS 35 stratospheric flight campaign in Sweden. One of the main objectives of the experiment was to investigate the electric currents [...] Read more.
This article describes the CURiE (Composites and photovoltaics Undergoing Radiation Exposure) stratospheric experiment, which was designed and built in 2024 for the BEXUS 35 stratospheric flight campaign in Sweden. One of the main objectives of the experiment was to investigate the electric currents generated in polycrystalline photovoltaic panels, shielded from visible light, and exposed in stratospheric conditions to cosmic radiation. The experiment’s registered data correlate with the X-ray fluxes registered by the GOES satellites, which are presented with the inclusion of the atmosphere’s attenuation. A single voltage-generating event may have been linked to the impact of a high-energy proton. The article forms a basis for the next research with the exposed photovoltaics and the next generation of experiments involving novel radiation-proof panels. Full article
(This article belongs to the Special Issue Compatibility, Power Electronics and Power Engineering)
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21 pages, 15561 KB  
Article
Semantic Communication on Digital Wireless Communication Systems
by Binhong Huang, Hao Chen, Cheng Guo, Xiaodong Xu, Nan Ma and Ping Zhang
Electronics 2025, 14(5), 956; https://doi.org/10.3390/electronics14050956 - 27 Feb 2025
Cited by 1 | Viewed by 1915
Abstract
Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of [...] Read more.
Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional bit error rate (BER). On the basis of the IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid joint source–channel coding (JSCC) and separate source–channel coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance, such as the Peak Signal-to-Noise Ratio (PSNR), in low Signal-to-Noise Ratio (SNR) environments while reducing bandwidth usage by up to 50% compared to the benchmark scheme. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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16 pages, 725 KB  
Article
How the Choice of LLM and Prompt Engineering Affects Chatbot Effectiveness
by Lukasz Pawlik
Electronics 2025, 14(5), 888; https://doi.org/10.3390/electronics14050888 - 24 Feb 2025
Cited by 5 | Viewed by 4762
Abstract
Modern businesses increasingly rely on chatbots to enhance customer communication and automate routine tasks. The research aimed to determine the optimal configurations of a telecommunications chatbot on the Rasa Pro platform, including the selection of large language models (LLMs), prompt formats, and command [...] Read more.
Modern businesses increasingly rely on chatbots to enhance customer communication and automate routine tasks. The research aimed to determine the optimal configurations of a telecommunications chatbot on the Rasa Pro platform, including the selection of large language models (LLMs), prompt formats, and command structures. The impact of various LLMs, prompt formats, and command precision on response quality was analyzed. Smaller models, like Gemini-1.5-Flash-8B and Gemma2-9B-IT, can achieve results comparable to larger models, offering a cost-effective solution. Specifically, the Gemini-1.5-Flash-8B model achieved an accuracy improvement of 21.62 points when using the JSON prompt format. This emphasizes the importance of prompt engineering techniques, like using structured formats (YAML, JSON) and precise commands. The study utilized a dataset of 400 sample test phrases created based on real customer service conversations with a mobile phone operator’s customers. Results suggest optimizing chatbot performance does not always require the most powerful models. Proper prompt preparation and data format choice are crucial. The theoretical framework focuses on the interaction between model size, prompt format, and command precision. Findings provide insights for chatbot designers to optimize performance through LLM selection and prompt construction. These findings have practical implications for businesses seeking cost-effective and efficient chatbot solutions. Full article
(This article belongs to the Special Issue New Trends in Artificial Neural Networks and Its Applications)
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26 pages, 18654 KB  
Article
A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
by Nurul I. Sarkar and Md Jahan Ali
Electronics 2025, 14(5), 872; https://doi.org/10.3390/electronics14050872 - 23 Feb 2025
Viewed by 2800
Abstract
Mobile ad hoc networks (MANETs) are becoming a popular networking technology as they can easily be set up and provide communication support on the go. These networks can be used in application areas, such as battlefields and disaster relief operations, where infrastructure networks [...] Read more.
Mobile ad hoc networks (MANETs) are becoming a popular networking technology as they can easily be set up and provide communication support on the go. These networks can be used in application areas, such as battlefields and disaster relief operations, where infrastructure networks are not available. Like media access control protocols, MANET routing protocols can also play an important role in determining network capacity and system performance. Research on the impact of heterogeneous nodes in terms of MANET performance is required for proper deployment of such systems. While MANET routing protocols have been studied and reported extensively in the networking literature, the performance of heterogeneous nodes/devices in terms of system performance has not been fully explored yet. The main objective of this paper is to review and compare the performance of four selected MANET routing protocols (AODV, OLSR, BATMAN and DYMO) in a heterogeneous MANET setting. We consider three different types of nodes in the MANET routing performance study, namely PDAs (fixed nodes with no mobility), laptops (low-mobility nodes) and mobile phones (high-mobility nodes). We measure the QoS metrics, such as the end-to-end delays, throughput, and packet delivery ratios, using the OMNeT++-network simulator. The findings reported in this paper provide some insights into MANET routing performance issues and challenges that can help network researchers and engineers to contribute further toward developing next-generation wireless networks capable of operating under heterogeneous networking constraints. Full article
(This article belongs to the Special Issue Multimedia in Radio Communication and Teleinformatics)
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19 pages, 8196 KB  
Article
Human–Robot Interaction Using Dynamic Hand Gesture for Teleoperation of Quadruped Robots with a Robotic Arm
by Jianan Xie, Zhen Xu, Jiayu Zeng, Yuyang Gao and Kenji Hashimoto
Electronics 2025, 14(5), 860; https://doi.org/10.3390/electronics14050860 - 21 Feb 2025
Cited by 7 | Viewed by 4044
Abstract
Human–Robot Interaction (HRI) using hand gesture recognition offers an effective and non-contact approach to enhancing operational intuitiveness and user convenience. However, most existing studies primarily focus on either static sign language recognition or the tracking of hand position and orientation in space. These [...] Read more.
Human–Robot Interaction (HRI) using hand gesture recognition offers an effective and non-contact approach to enhancing operational intuitiveness and user convenience. However, most existing studies primarily focus on either static sign language recognition or the tracking of hand position and orientation in space. These approaches often prove inadequate for controlling complex robotic systems. This paper proposes an advanced HRI system leveraging dynamic hand gestures for controlling quadruped robots equipped with a robotic arm. The proposed system integrates both semantic and pose information from dynamic gestures to enable comprehensive control over the robot’s diverse functionalities. First, a Depth–MediaPipe framework is introduced to facilitate the precise three-dimensional (3D) coordinate extraction of 21 hand bone keypoints. Subsequently, a Semantic-Pose to Motion (SPM) model is developed to analyze and interpret both the pose and semantic aspects of hand gestures. This model translates the extracted 3D coordinate data into corresponding mechanical actions in real-time, encompassing quadruped robot locomotion, robotic arm end-effector tracking, and semantic-based command switching. Extensive real-world experiments demonstrate the proposed system’s effectiveness in achieving real-time interaction and precise control, underscoring its potential for enhancing the usability of complex robotic platforms. Full article
(This article belongs to the Special Issue Communication Systems and Manipulators for Robots and Unmanned Systems)
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25 pages, 11298 KB  
Article
A Smart Space Focus Enhancement System Based on Grey Wolf Algorithm Positioning and Generative Adversarial Networks for Database Augmentation
by Jia-You Cai, Yu-Yong Luo and Chia-Hsin Cheng
Electronics 2025, 14(5), 865; https://doi.org/10.3390/electronics14050865 - 21 Feb 2025
Cited by 1 | Viewed by 794
Abstract
In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse [...] Read more.
In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse samples. This paper explores their application in improving datasets for indoor positioning and brainwave monitoring-based attention tracking. The goal is to develop an intelligent lighting system that adjusts settings based on users’ brainwave states and positions. GANs enhance brainwave monitoring and positioning datasets, with Principal Component Analysis (PCA) applied for dimensionality reduction. machine learning and deep learning models train on these augmented datasets, enabling dynamic lighting adjustments to optimize user experience. GANs undergo parameter fine-tuning to improve dataset quality. Various classification models, including neural networks (NN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), are used for brainwave monitoring, attention, and positioning. Fuzzy logic enhances system stability. The trained models are integrated with hardware components, such as the Raspberry Pi 4, to implement an “Indoor Positioning Deep Learning Brainwave Monitoring and Attention Monitoring System Based on the Grey Wolf Optimizer Algorithm”. Experimental results demonstrate a positioning accuracy of 15 cm and significant improvements in brainwave monitoring and attention tracking. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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16 pages, 3214 KB  
Article
Performance Comparison of Wired and Wireless Electrodermal Activity Sensors During a Simulated Port Approach Maneuver
by Dejan Žagar and Gregor Geršak
Electronics 2025, 14(5), 862; https://doi.org/10.3390/electronics14050862 - 21 Feb 2025
Viewed by 743
Abstract
On large ships, Officers on Watch (OOW) work in a demanding environment where they are confronted with stress and dynamic conditions and frequently move from one wing of the vessel to another. Therefore, a physiological monitoring system to assess the activity of their [...] Read more.
On large ships, Officers on Watch (OOW) work in a demanding environment where they are confronted with stress and dynamic conditions and frequently move from one wing of the vessel to another. Therefore, a physiological monitoring system to assess the activity of their autonomic nervous system, i.e., to detect their physiological responses, stress, and fatigue, should be lightweight and portable. This paper presents a comparison of wired and portable wearable psychophysiological systems. Although the wired system offers greater precision, its complexity, poor ergonomics, and the need for a controlled setup make it less suitable for the natural working conditions of OOWs. A wireless portable system, although weaker in precision, is more suitable due to its portability, ease of use, real-time data capabilities, ability to measure anxiety, and immediate insights into physiological states during real-world use in real time. Such an application provides a wearable physiological data collection solution that, in conjunction with a mobile app and cloud platform, enables seamless data collection and processing of participants’ autonomic nervous system arousal. Full article
(This article belongs to the Special Issue What Is Next in Wearable Computing for Mental and Physical Health Monitoring?)
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20 pages, 85541 KB  
Article
Fostering Inclusion: A Virtual Reality Experience to Raise Awareness of Dyslexia-Related Barriers in University Settings
by José Manuel Alcalde-Llergo, Pilar Aparicio-Martínez, Andrea Zingoni, Sara Pinzi and Enrique Yeguas-Bolívar
Electronics 2025, 14(5), 829; https://doi.org/10.3390/electronics14050829 - 20 Feb 2025
Cited by 2 | Viewed by 2031
Abstract
This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants [...] Read more.
This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students. Full article
(This article belongs to the Special Issue Virtual Reality Applications in Enhancing Human Lives)
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16 pages, 2665 KB  
Article
Development of New Generation Portable Camera-Aided Surgical Simulator for Cognitive Training in Laparoscopic Cholecystectomy
by Yucheng Li, Victoria Nelson, Cuong T. Nguyen, Irene Suh, Suvranu De, Ka-Chun Siu and Carl Nelson
Electronics 2025, 14(4), 793; https://doi.org/10.3390/electronics14040793 - 18 Feb 2025
Viewed by 938
Abstract
Laparoscopic cholecystectomy (LC) is the standard procedure for gallbladder removal, but improper identification of anatomical structures can lead to biliary duct injury (BDI). The critical view of safety (CVS) is a standardized technique designed to mitigate this risk. However, existing surgical training systems [...] Read more.
Laparoscopic cholecystectomy (LC) is the standard procedure for gallbladder removal, but improper identification of anatomical structures can lead to biliary duct injury (BDI). The critical view of safety (CVS) is a standardized technique designed to mitigate this risk. However, existing surgical training systems primarily emphasize haptic feedback and physical skill development, making them expensive and less accessible. This paper presents the next-generation Portable Camera-Aided Surgical Simulator (PortCAS), a cost-effective, portable, vision-based surgical training simulator designed to enhance cognitive skill acquisition in LC. The system consists of an enclosed physical module equipped with a vision system, a single-board computer for real-time instrument tracking, and a virtual simulation interface that runs on a user-provided computer. Unlike traditional simulators, PortCAS prioritizes cognitive training over force-based interactions, eliminating the need for costly haptic components. The system was evaluated through user studies assessing accuracy, usability, and training effectiveness. Results demonstrate that PortCAS provides a sufficiently accurate tracking performance for training surgical skills such as CVS, offering a scalable and accessible solution for surgical education. Full article
(This article belongs to the Special Issue Virtual Reality Applications in Enhancing Human Lives)
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21 pages, 3997 KB  
Article
Circuit-Centric Genetic Algorithm for the Optimization of a Radio-Frequency Receiver
by Hoyeon Shin, Mingi Kwon, Yeonjun Lee, Yeonggi Kim, Moon-Kyu Cho and Ickhyun Song
Electronics 2025, 14(4), 770; https://doi.org/10.3390/electronics14040770 - 16 Feb 2025
Viewed by 1667
Abstract
This paper presents a design automation method for optimizing parameters of radio-frequency front-ends, specifically aiming to maximize the overall performance of a receiver circuit. In this work, the design target includes a reduction in power consumption and noise figure and an increase in [...] Read more.
This paper presents a design automation method for optimizing parameters of radio-frequency front-ends, specifically aiming to maximize the overall performance of a receiver circuit. In this work, the design target includes a reduction in power consumption and noise figure and an increase in conversion gain. The use of an artificial algorithm for the optimization of an RF receiver is investigated, illustrating how to achieve performance goals in a complex design space composed of multiple inter-related circuit parameters. As the basis of the proposed research, the genetic algorithm, a well-known metaheuristic approach, is chosen and utilized in the optimization process. Since the conventional GA has limitations in circuit optimization, including suboptimal performance and slow convergence due to crossover operations, the concept of a circuit-centric genetic algorithm is proposed as a viable approach that primarily focuses on the use of a mutation process. In addition, by preserving high-performing solutions and incorporating a guiding mechanism toward metric-specific best solutions, the proposed method achieves the target performance much faster compared to other optimization approaches. Therefore, it can be utilized in the optimization of circuit parameter sets in RF receiver design. Full article
(This article belongs to the Special Issue Innovative Numerical Methods for Advanced Computation of Electromagnetic Devices and Microsystems)
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19 pages, 980 KB  
Article
A Comprehensive Analysis of Energy Consumption in Battery-Electric Buses Using Experimental Data: Impact of Driver Behavior, Route Characteristics, and Environmental Conditions
by Mattia Belloni, Davide Tarsitano and Edoardo Sabbioni
Electronics 2025, 14(4), 735; https://doi.org/10.3390/electronics14040735 - 13 Feb 2025
Cited by 3 | Viewed by 1977
Abstract
With the increasing emphasis on environmental sustainability, the electrification of urban public bus fleets has gained significant attention. Understanding the factors influencing the energy consumption of battery-electric buses (BEBs) is crucial for enhancing their energy efficiency. Therefore, it is crucial to identify the [...] Read more.
With the increasing emphasis on environmental sustainability, the electrification of urban public bus fleets has gained significant attention. Understanding the factors influencing the energy consumption of battery-electric buses (BEBs) is crucial for enhancing their energy efficiency. Therefore, it is crucial to identify the subsystems that contribute most to energy consumption and understand how operational factors influence them. This paper presents a comprehensive analysis of BEB energy consumption based on experimental measurements performed with a 12 m fully electric battery bus. The main limitations of this study stem from the use of a single vehicle over a total period of 18 days, during which 187 routes were completed. Additionally, sandbags were used as ballast in place of actual passengers. Various parameters, including the number of passengers, drivers, route characteristics, environmental conditions, and traffic, were analyzed to assess their impact on BEB energy consumption. Data related to the energy consumed by various bus utilities were collected through the vehicle’s CAN network, with a sampling rate of 1 measurement per second. These data were analyzed both daily and per route, revealing the breakdown of energy consumption among different utilities and highlighting those responsible for the highest energy use. The results correlate the total distance traveled, service duration, average speed, driver’s driving style, route characteristics, internal and external temperatures, and air-conditioning system’s reference temperature with the energy consumption of the traction motors and climate control system. In addition, the correlation between the driver, vehicle acceleration, and throttle pedal use, and the energy consumed by the electric traction motor is presented. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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21 pages, 4948 KB  
Article
Simultaneous Localization of Two Talkers Placed in an Area Surrounded by Asynchronous Six-Microphone Arrays
by Toru Takahashi, Taiki Kanbayashi and Masato Nakayama
Electronics 2025, 14(4), 711; https://doi.org/10.3390/electronics14040711 - 12 Feb 2025
Viewed by 2963
Abstract
If we can understand dialogue activities, it will be possible to know the role of each person in the discussion, and it will be possible to provide basic materials for formulating facilitation strategies. This understanding can be expected to be used for business [...] Read more.
If we can understand dialogue activities, it will be possible to know the role of each person in the discussion, and it will be possible to provide basic materials for formulating facilitation strategies. This understanding can be expected to be used for business negotiations, group work, active learning, etc. To develop a system that can monitor speech activity over a wide range of areas, we propose a method for detecting multiple acoustic events and localizing sound sources using an asynchronous distributed microphone array arranged in a regular hexagonal repeating structure. In contrast to conventional methods based on sound source direction using triangulation with microphone arrays, we propose a method for detecting acoustic events and determining sound sources from local maximum positions based on estimation of the spatial energy distribution inside the observation space. We evaluated the conventional method and the proposed method in an experimental environment in which a dialogue between two people was simulated under 22,104 conditions by using the sound source signal convolving the measured impulse response.We found that the performance changes depending on the selection of the microphone array used for estimation. Our finding is that it is best to choose five microphone arrays close to the evaluation position. Full article
(This article belongs to the Special Issue Recent Advances in Audio, Speech and Music Processing and Analysis, 2nd Edition)
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20 pages, 3455 KB  
Article
Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection
by Ahmad Ishaq, Fath U Min Ullah, Prince Hamandawana, Da-Jung Cho and Tae-Sun Chung
Electronics 2025, 14(4), 710; https://doi.org/10.3390/electronics14040710 - 12 Feb 2025
Cited by 10 | Viewed by 4202
Abstract
Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have [...] Read more.
Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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13 pages, 858 KB  
Article
Speech Enhancement Algorithm Based on Microphone Array and Multi-Channel Parallel GRU-CNN Network
by Ji Xi, Zhe Xu, Weiqi Zhang, Yue Xie and Li Zhao
Electronics 2025, 14(4), 681; https://doi.org/10.3390/electronics14040681 - 10 Feb 2025
Viewed by 1725
Abstract
This paper presents an improved speech enhancement algorithm based on microphone arrays to improve speech enhancement performance in complex settings. The algorithm’s model consists of two key components: the feature extraction module and the speech enhancement module. The feature extraction module processes the [...] Read more.
This paper presents an improved speech enhancement algorithm based on microphone arrays to improve speech enhancement performance in complex settings. The algorithm’s model consists of two key components: the feature extraction module and the speech enhancement module. The feature extraction module processes the speech amplitude spectral features derived from STFT (short-time Fourier transform). It employs parallel GRU-CNN (Gated Recurrent Units and CNN Convolutional Neural Network) structures to capture unique channel information, and skip connections are utilized to enhance the model’s convergence speed. The speech enhancement module focuses on obtaining cross-channel spatial information. By introducing an attention mechanism and applying a global hybrid pooling strategy, it reduces feature loss. This strategy dynamically assigns weights to each channel, emphasizing features that are most beneficial for speech signal restoration. Experimental results on the CHIME3 dataset show that the proposed model effectively suppresses diverse types of noise and outperforms other algorithms in improving speech quality and comprehension. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing for Diverse Applications)
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20 pages, 10544 KB  
Article
Modeling the Energy and Heating Efficiency of 3D Printing for Composite Materials with Dispersed Volumetric Particles
by Teodor Grakov, Valentin Mateev and Iliana Marinova
Electronics 2025, 14(4), 688; https://doi.org/10.3390/electronics14040688 - 10 Feb 2025
Cited by 1 | Viewed by 1643
Abstract
Additive manufacturing, such as the 3D printing of composite materials for electronics is rapidly evolving, enabling the production of advanced electric and magnetic composites with tailored properties. These materials require special printing conditions and advanced control to maintain the desired material properties during [...] Read more.
Additive manufacturing, such as the 3D printing of composite materials for electronics is rapidly evolving, enabling the production of advanced electric and magnetic composites with tailored properties. These materials require special printing conditions and advanced control to maintain the desired material properties during the 3D printing process and in the final product design. Hence, determining the heating and energy consumption and estimating the efficiency of 3D printing is essential. This work modeled the fused filament fabrication 3D printing of composite materials with a polymer carrier matrix. A 3D time-dependent thermal model of a 3D printer extruder was developed and implemented using the finite element method to study and improve the efficiency of 3D printing. As the filler content influences the operational parameters and process energy consumption of the 3D printing process, the transient heating process parameters were estimated using different composite modifier contents. Two types of modifiers were considered: Fe2O3 and CaO, both mixed in a PLA carrier material. The volumetric fill ratio of the two modifiers did not exceed 45%, as the mixing dependency of the material properties is linear in this range. The power fluxes and power efficiency were estimated. The results provide new possibilities for better control methodologies and advanced additive manufacturing for new materials in electronics. Operational control can accelerate the 3D printing process, speeding up the heating of 3D-printed composite materials and reducing the printing time and total energy consumption. Furthermore, this research provides directions for new advanced 3D printing extruder designs with better power and energy heating efficiency. Full article
(This article belongs to the Special Issue Recent Advances in 3D Printing Technologies and Applications for Electronics)
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30 pages, 7287 KB  
Article
Context-Aware Tomato Leaf Disease Detection Using Deep Learning in an Operational Framework
by Divas Karimanzira
Electronics 2025, 14(4), 661; https://doi.org/10.3390/electronics14040661 - 8 Feb 2025
Cited by 3 | Viewed by 2248
Abstract
Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based [...] Read more.
Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based classifier, specifically a Vision Transformer (ViT) integrated with cascaded group attention (CGA) and a modified Focaler-CIoU (Complete Intersection over Union) loss function. The proposed method aims to enhance the accuracy and robustness of disease detection by effectively capturing both local and global contextual information while addressing the challenges of sample imbalance in the dataset. To improve interpretability, we integrate Explainable Artificial Intelligence (XAI) techniques, enabling users to understand the rationale behind the model’s classifications. Additionally, we incorporate a large language model (LLM) to generate comprehensive, context-aware explanations and recommendations based on the identified diseases and other relevant factors, thus bridging the gap between technical analysis and user comprehension. Our evaluation against state-of-the-art deep learning methods, including convolutional neural networks (CNNs) and other transformer-based models, demonstrates that the ViT-CGA model significantly outperforms existing techniques, achieving an overall accuracy of 96.5%, an average precision of 93.9%, an average recall of 96.7%, and an average F1-score of 94.2% for tomato leaf disease classification. The integration of CGA and Focaler-CIoU loss not only contributes to improved model interpretability and stability but also empowers farmers and agricultural stakeholders with actionable insights, fostering informed decision making in disease management. This research advances the field of automated disease detection in crops and provides a practical framework for deploying deep learning solutions in agricultural settings, ultimately supporting sustainable farming practices and enhancing food security. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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12 pages, 3928 KB  
Article
Evaluation of a 1200 V Polarization Super Junction GaN Field-Effect Transistor in Cascode Configuration
by Alireza Sheikhan, E. M. Sankara Narayanan, Hiroji Kawai, Shuichi Yagi and Hironobu Narui
Electronics 2025, 14(3), 624; https://doi.org/10.3390/electronics14030624 - 5 Feb 2025
Cited by 1 | Viewed by 1361
Abstract
GaN HEMTs based on polarization super junction (PSJ) technology offer significant improvements in efficiency and power density over conventional silicon (Si) devices due to their excellent material characteristics, which enable fast switching edges and lower specific on-resistance. However, due to the presence of [...] Read more.
GaN HEMTs based on polarization super junction (PSJ) technology offer significant improvements in efficiency and power density over conventional silicon (Si) devices due to their excellent material characteristics, which enable fast switching edges and lower specific on-resistance. However, due to the presence of an uninterrupted channel between drain and source at zero gate bias, these devices have normally-on characteristics. In this paper, the performance of a 1200 V GaN FET utilizing PSJ technology in cascode configuration is reported. The device working principle, characteristics, and switching behavior are experimentally demonstrated. The results show that cascoded GaN FETs utilizing the PSJ concept are highly promising for power device applications. Full article
(This article belongs to the Special Issue GaN-Based Electronic Materials and Devices)
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19 pages, 1264 KB  
Article
Real-Time Adaptive and Lightweight Anomaly Detection Based on a Chaotic System in Cyber–Physical Systems
by Jung Kyu Park and Youngmi Baek
Electronics 2025, 14(3), 598; https://doi.org/10.3390/electronics14030598 - 3 Feb 2025
Cited by 1 | Viewed by 1588
Abstract
When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller [...] Read more.
When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller area networks (CANs) of CPSs makes it difficult to distinguish malicious data from legitimate data. The security vulnerabilities of CPSs, which are frequently exposed to adversaries, pose the risk of destabilizing the lives of humans. Therefore, we propose a real-time adaptive and lightweight anomaly detection (RALAD) mechanism that efficiently and securely detects anomalies within a given virtual group though verification of the data integrity and key management of stateless synchronization based on a chaotic system while driving. These characteristics prevent an adversary from authenticating maliciously modified messages even though it captures legitimate messages on the CAN bus. RALAD shows a clear difference from others in terms of (1) its unique secret key-sharing method and approach to secret key generation for each message, (2) safe controlling support after detecting anomalies, and (3) its software-based solution that eliminates the need for hardware secure modules. It leads to freedom from the issues of additional cost, weight, and wiring in CPSs. The proposed method achieves real-time anomaly detection, and the experiment results show a 100% detection rate for all attacks. This demonstrates that RALAD maintains high reliability and efficiency, even under various bus load conditions and attack rates. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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21 pages, 2637 KB  
Article
Molecular Layer Doping ZnO Films as a Novel Approach to Resistive Oxygen Sensors
by Wojciech Bulowski, Robert P. Socha, Anna Drabczyk, Patryk Kasza, Piotr Panek and Marek Wojnicki
Electronics 2025, 14(3), 595; https://doi.org/10.3390/electronics14030595 - 2 Feb 2025
Cited by 1 | Viewed by 1782
Abstract
In the modern world, gas sensors play a crucial role in sectors such as high-tech industries, medicine, and environmental monitoring. Among these fields, oxygen sensors are the most important. There are several types of oxygen sensors, including optical, magnetic, Schottky diode, and resistive [...] Read more.
In the modern world, gas sensors play a crucial role in sectors such as high-tech industries, medicine, and environmental monitoring. Among these fields, oxygen sensors are the most important. There are several types of oxygen sensors, including optical, magnetic, Schottky diode, and resistive (or chemoresistive) ones. Currently, most oxygen-resistive sensors (ORSs) described in the literature are fabricated as thick layers, typically deposited via screen printing, and they operate at high temperatures, often exceeding 700 °C. This work presents a novel approach utilizing atomic layer deposition (ALD) to create very thin layers. Combined with appropriate doping, this method aims to reduce the energy consumption of the sensors by lowering both the mass requiring heating and the operating temperature. The device fabricated using the proposed process demonstrates a response of 88.21 at a relatively low temperature of 450 °C, highlighting its potential in ORS applications based on doped ALD thin films. Full article
(This article belongs to the Special Issue High-Performance Gas Sensors: Materials, Technologies and Applications)
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15 pages, 6065 KB  
Article
Designing Spiking Neural Network-Based Reinforcement Learning for 3D Robotic Arm Applications
by Yuntae Park, Jiwoon Lee, Donggyu Sim, Youngho Cho and Cheolsoo Park
Electronics 2025, 14(3), 578; https://doi.org/10.3390/electronics14030578 - 31 Jan 2025
Cited by 2 | Viewed by 2884
Abstract
This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in [...] Read more.
This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in 3D robotic manipulation, demonstrating its extension from traditional 2D tasks to complex 3D target-reaching scenarios with improved energy efficiency and stability. Additionally, with the inertial measurement unit data the system successfully mimics human arm movements, achieving a success rate of 0.95 among 50 trials and enabling an intuitive and accurate human–robot interaction system. This pioneering attempt highlights the feasibility of combining the biologically inspired spiking neural networks with the reinforcement learning algorithm to address the real-time challenges in high-dimensional robotic environments and advance the field of human–robot interaction systems. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and Its Latest Applications)
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25 pages, 3311 KB  
Article
A VANET, Multi-Hop-Enabled, Dynamic Traffic Assignment for Road Networks
by Wilmer Arellano and Imad Mahgoub
Electronics 2025, 14(3), 559; https://doi.org/10.3390/electronics14030559 - 30 Jan 2025
Cited by 2 | Viewed by 1971
Abstract
Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a [...] Read more.
Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a subset of the Internet of Vehicles (IoV). The traffic assignment problem deals with road network traffic optimization. It is a complex and challenging problem. A few solutions incorporating VANET technology have been presented; most are centralized or depend on infrastructure. In previous work, we introduced Road-ACO, an ant colony optimization (ACO), single-hop, decentralized, infrastructure-less, VANET solution. In this paper, we propose a new multi-hop-enabled, decentralized, ant-colony-inspired algorithm for dynamic highway traffic assignment. The algorithm works for large road networks and requires no infrastructure. We develop Veins framework-based simulations to evaluate the algorithm’s performance. The results indicate that the proposed algorithm consistently outperforms Road-ACO and performs optimally on road segments up to 4000 m long, with improvements of up to 40% on average travel time. Full article
(This article belongs to the Special Issue Challenges and Opportunities in the Internet of Vehicles)
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17 pages, 4380 KB  
Article
Stroke Detection and Monitoring by Means of a Multifrequency Microwave Inversion Approach
by Alessandro Fedeli, Valentina Schenone, Claudio Estatico and Andrea Randazzo
Electronics 2025, 14(3), 543; https://doi.org/10.3390/electronics14030543 - 29 Jan 2025
Cited by 1 | Viewed by 1441
Abstract
In the area of biomedical diagnostics, microwave imaging techniques have been recently proposed for performing brain stroke detection and monitoring. Indeed, theoretically, these techniques make it possible to meet the timeliness requirements of such a diagnosis with portable systems. Moreover, relying on the [...] Read more.
In the area of biomedical diagnostics, microwave imaging techniques have been recently proposed for performing brain stroke detection and monitoring. Indeed, theoretically, these techniques make it possible to meet the timeliness requirements of such a diagnosis with portable systems. Moreover, relying on the use of microwaves, they are noninvasive and allow continuous monitoring of critical patients. In this paper, the microwave imaging problem is solved by exploiting multifrequency data by an inexact-Newton method formulated in the framework of non-constant exponent Lebesgue spaces. First, the method is numerically validated with three-dimensional head models affected by anatomically-realistic strokes. Then, a further assessment through experimental data obtained with a cylindrical phantom is conducted. A quite accurate reconstruction of the variations of dielectric properties inside the patient’s head due to the insurgence of stroke is obtained in both numerical and experimental cases, showing the potentiality of the proposed approach. Full article
(This article belongs to the Special Issue Electromagnetic Imaging from Radio Frequency to Sub-millimeter Waves)
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17 pages, 9345 KB  
Article
Iterative Learning Control Design for a Class of Mobile Robots
by Dominik Zaborniak, Piotr Balik, Kacper Woźniak, Bartłomiej Sulikowski and Marcin Witczak
Electronics 2025, 14(3), 531; https://doi.org/10.3390/electronics14030531 - 28 Jan 2025
Cited by 1 | Viewed by 1501
Abstract
The paper presents the design of iterative learning control for a class of mobile robots. This control strategy allows driving the considered system, which executes the same control task in trials, to the predefined reference within the consecutive iterations by improving the control [...] Read more.
The paper presents the design of iterative learning control for a class of mobile robots. This control strategy allows driving the considered system, which executes the same control task in trials, to the predefined reference within the consecutive iterations by improving the control signal gradually. The control problem being stated concerns a mobile robot, and hence, its kinematic model is presented. The considered model is nonlinear as it is related to the robot orientation angle. Thus, the linearization strategy is introduced by dividing the range of possible orientation angles to four quarters and then deriving a linear parameter-varying system. As a distinct research topic, the feasible/optimal number selection of polytope vertices of each LPV submodel are considered. Next, for the resulting bank of models, the switched iterative control scheme is transformed into closed-loop differential linear repetitive processes. Subsequently, based on the fact that ensuring the so-called stability along the trial is equivalent to the convergence of the original model output to the predefined reference, an appropriate stabilization condition is applied in order to compute the feedback controller gains. The overall effectiveness and performance of the proposed methodology are evaluated through comprehensive simulation examples. Full article
(This article belongs to the Section Systems & Control Engineering)
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19 pages, 4022 KB  
Article
Framework for Analyzing Spatial Interference in Vehicle-to-Vehicle Communication Networks with Positional Errors
by Nivetha Kanthasamy and Alexander Wyglinski
Electronics 2025, 14(3), 510; https://doi.org/10.3390/electronics14030510 - 26 Jan 2025
Cited by 2 | Viewed by 1195
Abstract
This paper introduces a novel framework for evaluating vehicle-to-vehicle (V2V) communication systems, employing beamforming and null steering techniques. Addressing challenges such as electromagnetic interference from other vehicles within the network as well as diverse road conditions, the framework defines the simulation environment for [...] Read more.
This paper introduces a novel framework for evaluating vehicle-to-vehicle (V2V) communication systems, employing beamforming and null steering techniques. Addressing challenges such as electromagnetic interference from other vehicles within the network as well as diverse road conditions, the framework defines the simulation environment for V2V networks across different traffic scenarios to assess system reliability. The analytical components of the proposed framework are structured as follows: a comprehensive framework is developed, serving as the basis for implementing a simulator that leverages advanced spatial signal processing algorithms to evaluate V2V networks across specific scenarios, accounting for the effects of positional errors. The framework integrates multiple blocks, including road modeling, system modeling, and performance evaluation, providing adaptability for different algorithms or configurations. Positional inaccuracies are examined, highlighting their effects on system performance, particularly in scenarios where null steering accuracy is imperfect, thus underscoring the need for enhanced interference management strategies. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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21 pages, 515 KB  
Article
Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection
by Chathuranga Sampath Kalutharage, Saket Mohan, Xiaodong Liu and Christos Chrysoulas
Electronics 2025, 14(3), 474; https://doi.org/10.3390/electronics14030474 - 24 Jan 2025
Cited by 2 | Viewed by 1507
Abstract
The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investigates the application of CHERI (Capability Hardware Enhanced RISC Instructions) to enhance [...] Read more.
The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investigates the application of CHERI (Capability Hardware Enhanced RISC Instructions) to enhance the security of Intrusion Detection Systems (IDSs) in automotive networks. By leveraging CHERI’s fine-grained memory protection and capability-based access control, the IDS ensures the robust protection of rule configurations against unauthorized access and manipulation. Experimental results demonstrate a 100% detection rate for spoofed IP packets and unauthorized rule modification attempts. The CHERI-enabled IDS framework achieves latency well within the acceptable limits defined by automotive standards for real-time applications, ensuring it remains suitable for safety-critical operations. The implementation on the ARM Morello board highlights CHERI’s practical applicability and low-latency performance in real-world automotive scenarios. This research underscores the potential of hardware-enforced memory safety in mitigating complex cyber threats and provides a scalable solution for securing increasingly connected and autonomous vehicles. Future work will focus on optimizing CHERI for resource-constrained environments and expanding its applications to broader automotive security use cases. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 2nd Edition)
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14 pages, 4022 KB  
Article
A 13–33 GHz Wideband Low-Noise Amplifier in 150-nm GaAs Based on Simultaneous Noise- and Input-Matched Gain-Core with R-L-C Shunt Feedback Network
by Seonyeong Hwang, Dongwan Kang, Yeonggeon Lee and Dae-Woong Park
Electronics 2025, 14(3), 450; https://doi.org/10.3390/electronics14030450 - 23 Jan 2025
Cited by 2 | Viewed by 1544
Abstract
This work reports the concept of a shunt negative feedback technique for implementing a millimeter-wave wideband low-noise amplifier. The proposed shunt negative feedback network consists of a resistor–capacitor–inductor configuration. The proposed feedback network can achieve simultaneous noise and input matching (SNIM) over a [...] Read more.
This work reports the concept of a shunt negative feedback technique for implementing a millimeter-wave wideband low-noise amplifier. The proposed shunt negative feedback network consists of a resistor–capacitor–inductor configuration. The proposed feedback network can achieve simultaneous noise and input matching (SNIM) over a wide frequency range by adjusting the values of the resistor–capacitor–inductor configuration based on numerical analysis. By adopting the SNIM-based gain core as the first stage of the amplifier, the simulation results of the three-stage low-noise amplifier in a 150-nm GaAs pHEMT process achieve a gain of 15.6–18.6 dB and a noise figure of 1.05–2.8 dB in the frequency range of 13–33 GHz, respectively, while dissipating 99 mW. Full article
(This article belongs to the Special Issue RF/MM-Wave Circuits Design and Applications, 2nd Edition)
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11 pages, 2946 KB  
Article
Negative Capacitance Analysis of Multi-Quantum-Well Light-Emitting Diodes
by Yang Xiao, Xiaoyu Feng, Yuan Meng, Longzhen He, Pengzhe Zhang, Dongyan Zhang, Shoushuai Gao, Philip Shields, Haitao Tian and Hongwei Liu
Electronics 2025, 14(3), 413; https://doi.org/10.3390/electronics14030413 - 21 Jan 2025
Viewed by 1052
Abstract
To explain the negative capacitance (NC) characteristic of multi-quantum-well (MQW) LEDs, we calculated the continuity equation for a 10-period AlGaInP/GaInP multi-quantum-well (MQW) LED with a mesa size of 90 × 150 μm and build an MQW LED capacitor model. The carrier concentrations and [...] Read more.
To explain the negative capacitance (NC) characteristic of multi-quantum-well (MQW) LEDs, we calculated the continuity equation for a 10-period AlGaInP/GaInP multi-quantum-well (MQW) LED with a mesa size of 90 × 150 μm and build an MQW LED capacitor model. The carrier concentrations and capacitance–voltage characteristics across every quantum well region were analyzed by accounting for carrier spontaneous, Auger, and SRH recombination. In our model, a dynamic carrier lifetime iteration method with an iterative error of less than 1 × 10−14 ns was used to decouple carrier lifetime and electric field variables in the carrier continuity equation, providing a new way to enhance the accuracy of MQW continuous equations. Based on the calculated carrier concentration and lifetime of MQWs, we derived a multilayer epitaxial material LED capacitance equivalent circuit. The theoretical model characterizes the negative capacitance phenomenon at 1.75 V, which is consistent with the actual test results of the sample. Our theoretical analysis indicates that the negative capacitance mainly comes from the carrier recombination in the MQW region. Under low-frequency AC bias conditions, the negative capacitance phenomenon becomes more obvious. This work provides a useful reference for analyzing the capacitance and bandwidth characteristics of LEDs in the fields of display dimming and visible-light communication. Full article
(This article belongs to the Section Optoelectronics)
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13 pages, 10090 KB  
Article
Dual-Band Dual-Circularly Polarized Shared-Aperture Phased Array for S-/C-Band Satellite Communications
by Yuanming Xiao, Lianxing He and Xiaoli Wei
Electronics 2025, 14(2), 387; https://doi.org/10.3390/electronics14020387 - 20 Jan 2025
Cited by 3 | Viewed by 2009
Abstract
In this article, a novel method of achieving a single-layer, dual-band, dual-circularly polarized (CP) shared-aperture phased array antenna with wide beam scanning coverage is presented. The space antenna was designed to provide direct-to-cellular communications services at S-/C-bands with a frequency ratio of 1:1.8. [...] Read more.
In this article, a novel method of achieving a single-layer, dual-band, dual-circularly polarized (CP) shared-aperture phased array antenna with wide beam scanning coverage is presented. The space antenna was designed to provide direct-to-cellular communications services at S-/C-bands with a frequency ratio of 1:1.8. Using novel ceramic substrates with high dielectric constants for antenna miniaturization, the optimum interelement spacing can be ensured in one single layer to meet the large-angle scanning demand. The CP characteristic of the phased array is improved by the sequential rotation technique. A prototype of phased array, which is composed of an 8 × 8 S-band Rx array and a 16 × 16 C-band Tx array, is fabricated to verify this design. The measured results show that the shared-aperture phased array can provide ±50° beam scanning coverage at both the S- and C-bands simultaneously to meet the direct-to-cellular communication demand in low earth orbit (LEO) satellites. Full article
(This article belongs to the Special Issue Antenna Designs for 5G/IoT and Space Applications, 2nd Edition)
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32 pages, 8060 KB  
Article
Study on Robust Path-Tracking Control for an Unmanned Articulated Road Roller Under Low-Adhesion Conditions
by Wei Qiang, Wei Yu, Quanzhi Xu and Hui Xie
Electronics 2025, 14(2), 383; https://doi.org/10.3390/electronics14020383 - 19 Jan 2025
Cited by 2 | Viewed by 1457
Abstract
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of [...] Read more.
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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24 pages, 8199 KB  
Article
Redefining 6G Network Slicing: AI-Driven Solutions for Future Use Cases
by Robert Botez, Daniel Zinca and Virgil Dobrota
Electronics 2025, 14(2), 368; https://doi.org/10.3390/electronics14020368 - 18 Jan 2025
Cited by 10 | Viewed by 5096
Abstract
The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive [...] Read more.
The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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12 pages, 1069 KB  
Article
A GNN-Based Placement Optimization Guidance Framework by Physical and Timing Prediction
by Peng Cao, Zhi Li and Wenjie Ding
Electronics 2025, 14(2), 329; https://doi.org/10.3390/electronics14020329 - 15 Jan 2025
Viewed by 1986
Abstract
Placement is crucial in physical design flow with significant impact on later routability and ultimate manufacturability in terms of performance, power, and area (PPA), which may deviate from finding the optimal solution and/or lead to unnecessary iterations suffering from interleaved optimization steps and [...] Read more.
Placement is crucial in physical design flow with significant impact on later routability and ultimate manufacturability in terms of performance, power, and area (PPA), which may deviate from finding the optimal solution and/or lead to unnecessary iterations suffering from interleaved optimization steps and inaccurate PPA estimation. To solve this issue, we propose a physical- and timing-related placement optimization guidance framework which provides candidate gate sizing and buffer insertion solutions as well as a path group for potential violated paths based on graph neural networks (GNNs) to improve placement quality significantly and efficiently. Experimental results on the OpenCores benchmarks with 22 nm technology demonstrate that the proposed placement optimization guidance framework achieves up to 35.66% and 43.51% worst negative slack (WNS) and total negative slack (TNS) improvement and 52.17% reduction in the number of violating paths (NVP), which is beneficial to later routing stages with 2.33% wirelength decrease. Full article
(This article belongs to the Special Issue Innovations in Digital and Analog Electronic Systems for Next-Generation Devices)
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12 pages, 4532 KB  
Article
Regression Analysis for Predicting the Magnetic Field Shielding Effectiveness of Ferrite Sheets
by Hyun Ho Park, Heehyuk Lee and Deuk-Kyu Hwang
Electronics 2025, 14(2), 310; https://doi.org/10.3390/electronics14020310 - 14 Jan 2025
Viewed by 959
Abstract
In this paper, a method to predict near-field magnetic shielding effectiveness (NSE) of ferrite sheets is proposed by measuring their relative permeability. The NSE prediction for ferrite sheets is developed using eight regression models based on higher-order terms of permeability, extracted through Minitab’s [...] Read more.
In this paper, a method to predict near-field magnetic shielding effectiveness (NSE) of ferrite sheets is proposed by measuring their relative permeability. The NSE prediction for ferrite sheets is developed using eight regression models based on higher-order terms of permeability, extracted through Minitab’s regression analysis using data from the measured NSE and relative permeabilities of the ferrite sheets. To analyze the accuracy of the predicted NSE in comparison to the measured NSE, the mean square error (MSE) was computed. As a result, the extracted regression models enable fast and accurate NSE predictions for ferrite sheets up to 100 MHz, achieving an MSE of less than 1.0, in contrast to numerical simulation methods that require several hours. Full article
(This article belongs to the Special Issue Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition)
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20 pages, 4123 KB  
Article
RFID Unpacked: A Case Study in Employing RFID Tags from Item to Pallet Level
by Ethan Claucherty, Danielle Cummins and Bahar Aliakbarian
Electronics 2025, 14(2), 278; https://doi.org/10.3390/electronics14020278 - 11 Jan 2025
Viewed by 3041
Abstract
As the use of passive ultra-high frequency (UHF) radio frequency identification (RFID) tags continues to surge in supply chain management, it becomes crucial to optimize their application at various levels of packaging to ensure reliability. These packaging levels play a pivotal role in [...] Read more.
As the use of passive ultra-high frequency (UHF) radio frequency identification (RFID) tags continues to surge in supply chain management, it becomes crucial to optimize their application at various levels of packaging to ensure reliability. These packaging levels play a pivotal role in achieving maximum readability and widespread adoption within the industry. This research paper aims to determine the most suitable passive UHF RFID tag for consumer goods filled with liquid and wrapped in foil packaging. In this study, two distinct RFID tags from separate manufacturers were evaluated. The research focused on critical factors such as reader height, distance, and item configuration across different packaging levels (item, case, and pallet). The results demonstrated that the packaging configuration impacts the readability of RFID tags at each packaging level. Through rigorous testing, it was found that achieving a tag readability rate higher than 99.7% is feasible and readability can be optimized by adjusting the reader position, packaging configuration, and tag design. The optimized configuration and testing platform developed in this study can be used for comparable products in other supply chains such as consumer goods, pharmaceuticals, and food. The results of this study emphasize RFID’s potential to revolutionize supply chain management. Full article
(This article belongs to the Special Issue RFID Technology and Its Applications)
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11 pages, 26868 KB  
Article
Wearable Displacement Sensor Using Inductive Coupling of Printed RFID Tag with Metallic Strip
by Tauseef Hussain, Ignacio Gil and Raúl Fernández-García
Electronics 2025, 14(2), 262; https://doi.org/10.3390/electronics14020262 - 10 Jan 2025
Cited by 2 | Viewed by 3833
Abstract
This paper presents a passive displacement sensor based on the inductive coupling between a printed UHF RFID tag and a metallic strip. The sensor operates by exploiting variations in mutual inductive coupling, which modulate the tag impedance and transmission coefficient, thereby altering the [...] Read more.
This paper presents a passive displacement sensor based on the inductive coupling between a printed UHF RFID tag and a metallic strip. The sensor operates by exploiting variations in mutual inductive coupling, which modulate the tag impedance and transmission coefficient, thereby altering the backscattered signal strength and the maximum read range of the RFID tag. The performance of the sensor is validated through simulations and experiments, which demonstrate a sensitivity characterized by an approximately 9 dB reduction in the received signal strength indicator (RSSI) and a 2.3 m decrease in the read range within the first 12 mm of displacement. Furthermore, its potential for wearable applications is showcased through respiratory monitoring, where RSSI variations of approximately 5 dB are observed between the inspiration and expiration phases when positioned on the abdominal region of a volunteer. Thus, the proposed displacement sensing approach offers a cost-effective and battery-free solution for wearable applications with remote monitoring capabilities. Full article
(This article belongs to the Special Issue RFID Technology and Its Applications)
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28 pages, 1601 KB  
Review
Methods and Approaches for User Engagement and User Experience Analysis Based on Electroencephalography Recordings: A Systematic Review
by Christos Bellos, Konstantinos Stefanou, Alexandros Tzallas, Georgios Stergios and Markos Tsipouras
Electronics 2025, 14(2), 251; https://doi.org/10.3390/electronics14020251 - 9 Jan 2025
Cited by 3 | Viewed by 2681
Abstract
This review paper explores the intersection of user engagement and user experience studies with electroencephalography (EEG) analysis by investigating the existing literature in this field. User engagement describes the immediate, session-based experience of using interactive products and is commonly used as a metric [...] Read more.
This review paper explores the intersection of user engagement and user experience studies with electroencephalography (EEG) analysis by investigating the existing literature in this field. User engagement describes the immediate, session-based experience of using interactive products and is commonly used as a metric to assess the success of games, online platforms, applications, and websites, while user experience encompasses the broader and longer-term aspects of user interaction. This review focuses on the use of EEG as a precise and objective method to gain insights into user engagement. EEG recordings capture brain activity as waves, which can be categorized into different frequency bands. By analyzing patterns of brain activity associated with attention, emotion, mental workload, and user experience, EEG provides valuable insights into user engagement. The review follows the PRISMA statement. The search process involved an extensive exploration of multiple databases, resulting in the identification of 74 relevant studies. The review encompasses the entire information flow of the experiments, including data acquisition, pre-processing analysis, feature extraction, and analysis. By examining the current literature, this review provides a comprehensive overview of various algorithms and processes utilized in EEG-based systems for studying user engagement and identifies potential directions for future research endeavors. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)
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26 pages, 1704 KB  
Article
A Unified Design Methodology for Front-End RF/mmWave Receivers
by Anastasios Michailidis, Alexandros Chatzis, Panayiota Tsimpou, Vasiliki Gogolou and Thomas Noulis
Electronics 2025, 14(2), 235; https://doi.org/10.3390/electronics14020235 - 8 Jan 2025
Cited by 1 | Viewed by 1495
Abstract
In this work, a unified design methodology for front-end RF/mmWave receivers is presented, aiming to significantly accelerate the design procedure of the front-end RF blocks in complex RX/TX chain implementations. The proposed design methodology is based on optimization loops with well-defined cost functions [...] Read more.
In this work, a unified design methodology for front-end RF/mmWave receivers is presented, aiming to significantly accelerate the design procedure of the front-end RF blocks in complex RX/TX chain implementations. The proposed design methodology is based on optimization loops with well-defined cost functions so as to minimize the design iterations that may be encountered during specification tuning. As proof of concept, two essential RF blocks widely used in RF receivers, a low-noise amplifier (LNA) and a voltage-controlled oscillator (VCO), were designed using the proposed unified methodology with a 65 nm RF-CMOS processing node. Finally, the derived designs were compared to similar designs in the literature, proving that the proposed unified methodology is capable of synthesizing RF/mmWave LNAs and VCOs with industry-standard specifications within a significantly faster time frame. Full article
(This article belongs to the Special Issue RF/MM-Wave Circuits Design and Applications, 2nd Edition)
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17 pages, 13090 KB  
Article
Dynamic Imaging of Projected Electric Potentials of Operando Semiconductor Devices by Time-Resolved Electron Holography
by Tolga Wagner, Hüseyin Çelik, Simon Gaebel, Dirk Berger, Peng-Han Lu, Ines Häusler, Nina Owschimikow, Michael Lehmann, Rafal E. Dunin-Borkowski, Christoph T. Koch and Fariba Hatami
Electronics 2025, 14(1), 199; https://doi.org/10.3390/electronics14010199 - 5 Jan 2025
Cited by 2 | Viewed by 1974
Abstract
Interference gating (iGate) has emerged as a groundbreaking technique for ultrafast time-resolved electron holography in transmission electron microscopy, delivering nanometer spatial and nanosecond temporal resolution with minimal technological overhead. This study employs iGate to dynamically observe the local projected electric potential within the [...] Read more.
Interference gating (iGate) has emerged as a groundbreaking technique for ultrafast time-resolved electron holography in transmission electron microscopy, delivering nanometer spatial and nanosecond temporal resolution with minimal technological overhead. This study employs iGate to dynamically observe the local projected electric potential within the space-charge region of a contacted transmission electron microscopy (TEM) lamella manufactured from a silicon diode during switching between unbiased and reverse-biased conditions, achieving a temporal resolution of 25 ns at a repetition rate of 3 MHz. By synchronizing the holographic acquisition with the applied voltage, this approach enables the direct visualization of time-dependent potential distributions with high precision. Complementary static and dynamic experiments reveal a remarkable correspondence between modeled and measured projected potentials, validating the method’s robustness. The observed dynamic phase progressions resolve and allow one to differentiate between localized switching dynamics and preparation-induced effects, such as charge recombination near the sample edges. These results establish iGate as a transformative tool for operando investigations of semiconductor devices, paving the way for advancing the nanoscale imaging of high-speed electronic processes. Full article
(This article belongs to the Section Optoelectronics)
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21 pages, 1422 KB  
Article
Multi-Agent Reinforcement Learning for Efficient Resource Allocation in Internet of Vehicles
by Jun-Han Wang, He He, Jaesang Cha, Incheol Jeong and Chang-Jun Ahn
Electronics 2025, 14(1), 192; https://doi.org/10.3390/electronics14010192 - 5 Jan 2025
Cited by 4 | Viewed by 4106
Abstract
The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional [...] Read more.
The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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13 pages, 5715 KB  
Communication
Enhanced Short-Circuit Robustness of 1.2 kV Split Gate Silicon Carbide Metal Oxide Semiconductor Field-Effect Transistors for High-Frequency Applications
by Kanghee Shin, Dongkyun Kim, Minu Kim, Junho Park and Changho Han
Electronics 2025, 14(1), 163; https://doi.org/10.3390/electronics14010163 - 3 Jan 2025
Viewed by 2152
Abstract
Split Gate SiC MOSFETs (SG-MOSFETs) have been demonstrated to exhibit excellent power dissipation at high operating frequencies due to their low specific reverse transfer capacitance (Crss,sp); however, there are several reliability issues of SG-MOSFETs, including electric field crowding at the [...] Read more.
Split Gate SiC MOSFETs (SG-MOSFETs) have been demonstrated to exhibit excellent power dissipation at high operating frequencies due to their low specific reverse transfer capacitance (Crss,sp); however, there are several reliability issues of SG-MOSFETs, including electric field crowding at the gate oxide and insufficient short-circuit (SC) robustness. In this paper, we propose a device structure to enhance the short-circuit withstand time (SCWT) of 1.2 kV SG-MOSFETs. The proposed P-shielded SG-MOSFETs (PSG-MOSFETs) feature a P-shielding region that expands the depletion region within the JFET region under both blocking mode and SC conditions. Compared to the conventional structure, this reduces the maximum electric field in the gate oxide, enabling a higher doping concentration in the JFET region, which can reduce the specific on-resistance (Ron,sp) to minimize power dissipation during device operation. The SC robustness of PSG-MOSFETs, with an Ron,sp identical to those of SG-MOSFETs, was investigated by adjusting the width of the P-shielding region (WP). Furthermore, the Crss,sp of PSG-MOSFETs was compared with that of SG-MOSFETs to analyze the relationship between the WP and high-frequency figure of merit (HF-FOM), defined as Ron,sp × Crss,sp. These results demonstrated that the PSG-MOSFET achieved an enhanced SC robustness and HF-FOM in comparison to the SG-MOSFET. Thus, the proposed PSG-MOSFET is a highly suitable candidate for high-frequency and reliable applications. Full article
(This article belongs to the Special Issue Wide-Bandgap Device Application: Devices, Circuits, and Drivers)
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21 pages, 2394 KB  
Article
AFHRE: An Accurate and Fast Hardware Resources Estimation Method for Convolutional Accelerator with Systolic Array Structure on FPGA
by Yongchang Wang, Hongzhi Zhao and Jinyao Zhao
Electronics 2025, 14(1), 168; https://doi.org/10.3390/electronics14010168 - 3 Jan 2025
Viewed by 1098
Abstract
FPGA-based convolutional accelerators have been widely used in image recognition scenarios. Many convolutional accelerators utilize the systolic array structure to enhance parallelism. Developing a method to efficiently estimate the utilized hardware resources of an FPGA for such a structure would be helpful in [...] Read more.
FPGA-based convolutional accelerators have been widely used in image recognition scenarios. Many convolutional accelerators utilize the systolic array structure to enhance parallelism. Developing a method to efficiently estimate the utilized hardware resources of an FPGA for such a structure would be helpful in improving the speed of achieving an optimal systolic array structure with the best performance on a given FPGA device. Currently, most estimations of work have either focused on the evaluation of hardware resources for general structures or have not adequately assessed hardware resources specifically for systolic arrays. To reduce estimation latency, this paper proposes an Accurate and Fast Hardware Resources Estimation method (AFHRE) that addresses these shortcomings by analyzing the structure of systolic arrays and utilizing mathematical formulas to describe their characteristics. Experiments show that the DSP resource occupancy estimated by AFHRE is fully consistent with that by Vivado HLS. The error rates of other three types of hardware resources (BRAM, LUT, and FF) are within 11%. In addition, the speed of resource estimation using this method is 40X to 610X faster than that of Vivado HLS. AFHRE can serve as a preprocessing step for Vivado HLS, achieving some optimal or sub-optimal solutions systolic array parameters much faster than original simulation manners of Vivado HLS. Full article
(This article belongs to the Special Issue FPGA-Based Reconfigurable Embedded Systems)
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32 pages, 2525 KB  
Article
Cyberthreats and Security Measures in Drone-Assisted Agriculture
by Kyriaki A. Tychola and Konstantinos Rantos
Electronics 2025, 14(1), 149; https://doi.org/10.3390/electronics14010149 - 2 Jan 2025
Cited by 6 | Viewed by 3794
Abstract
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the [...] Read more.
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the necessity of this technology is increasing, given its numerous benefits. However, as with any modern and automated technology, security concerns arise from various aspects. In this paper, we discuss cyberthreats to drones, as this technology is vulnerable to attackers during data collection, storage, and usage. Although various techniques and methods have been developed to address attacks on drones, this field remains in its infancy in many respects. This paper provides a comprehensive review of the security challenges associated with the use of agricultural drones. The security issues were thoroughly analyzed, with a particular focus on cybersecurity, categorized into four distinct levels: emerging threats, sensor vulnerabilities, hardware and software attacks, and communication-related threats. Additionally, we examined the limitations and challenges posed by cyberthreats to drone systems. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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37 pages, 4815 KB  
Article
Autonomous Forklifts: State of the Art—Exploring Perception, Scanning Technologies and Functional Systems—A Comprehensive Review
by Muftah A Fraifer, Joseph Coleman, James Maguire, Petar Trslić, Gerard Dooly and Daniel Toal
Electronics 2025, 14(1), 153; https://doi.org/10.3390/electronics14010153 - 2 Jan 2025
Cited by 8 | Viewed by 7593
Abstract
This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, [...] Read more.
This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, while also highlighting the latest developments in both academic research and industrial applications. Given the critical importance of object detection and recognition in machine vision and autonomous vehicles, this area receives particular attention. The article provides an in-depth summary of both commercial and prototype forklifts, discussing key aspects such as design features, capabilities and benefits, and offers a detailed technical comparison. Specifically, it clarifies that all available data pertains to commercially available forklifts. To obtain a better understanding of the current state-of-the-art and its limitations, the analysis also reviews commercially available autonomous forklifts. Finally, this paper includes a comprehensive bibliography of research findings in this field. Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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16 pages, 652 KB  
Article
A Novel Architecture for Virtual Network Twin Deployment
by Amir Hossein Banisadr and Xavier Hesselbach
Electronics 2024, 13(24), 5045; https://doi.org/10.3390/electronics13245045 - 22 Dec 2024
Viewed by 937
Abstract
In this paper, a novel architecture is proposed that enhances network connectivity by combining network virtualization and the digital twin approach. A virtual network twin (VNT) framework is designed to emulate the behavior of the original network within a virtualized environment. This framework [...] Read more.
In this paper, a novel architecture is proposed that enhances network connectivity by combining network virtualization and the digital twin approach. A virtual network twin (VNT) framework is designed to emulate the behavior of the original network within a virtualized environment. This framework provides an enhanced connection experience for users, mirroring the performance of the original network while avoiding the limitations of previous methods such as Telnet, SSH, and VPN. By integrating the network virtualization approach and the concept of digital twins, this framework can improve network visibility, security, and robustness in real-time connectivity through appropriate communication protocols and artificial intelligence (AI) methods. The application of this proposal can significantly impact key areas such as medical applications, autonomous driving, and space communications. This paper introduces the VNT architecture; its core components; requirements; and evaluation metrics, such as the accuracy of the VNT. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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12 pages, 2236 KB  
Article
Novel Indoor Educational I-V Tracer for Photovoltaic Modules
by Jose Vicente Muñoz, Luis Miguel Nieto, Juan Francisco Canalejo, Jesus Montes-Romero, Angel Gaspar Gonzalez-Rodriguez and Slawomir Gulkowski
Electronics 2024, 13(24), 4932; https://doi.org/10.3390/electronics13244932 - 13 Dec 2024
Viewed by 1421
Abstract
The renewable energy market, particularly the photovoltaic sector, has experienced significant growth over the past decade. Higher education institutions must play a vital role in the training of professionals, which the sector is currently demanding and will continue to require in the future. [...] Read more.
The renewable energy market, particularly the photovoltaic sector, has experienced significant growth over the past decade. Higher education institutions must play a vital role in the training of professionals, which the sector is currently demanding and will continue to require in the future. A pivotal resource for understanding the performance of PV modules is the experimental extraction of the characteristic I-V curve in laboratory practices. This paper presents an innovative and low-cost I-V curve tracer which can be used in indoor laboratories for teaching purposes. The described measurement system presents the novelty of helping form an energy-harvesting IC to force a sweep of the voltage from values close to zero to the open voltage circuit (Voc). An Arduino Micro board interfaces the implemented electronics and a LabVIEW-based monitoring and control program. The system proved its reliability and accuracy when it was compared to a calibrated commercial I-V tracer. The experimental results show that for a low-power PV module illuminated by a lamp, the proposed I-V tracer only deviated 1.3% from the commercial one in measurements of the maximum power. Full article
(This article belongs to the Special Issue Trends in Photovoltaic Systems for Enhanced Power Generation and Energy Efficiency)
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25 pages, 11970 KB  
Article
General Obstacle Avoidance Capability Assessment for Autonomous Vehicles
by Evan Lowe and Levent Guvenc
Electronics 2024, 13(24), 4901; https://doi.org/10.3390/electronics13244901 - 12 Dec 2024
Viewed by 1263
Abstract
As autonomous vehicle (AV) capabilities expand, it is important to ensure their safety during testing and deployment for public usage. While several testing regulations have been proposed in research, US federal, and even global guidelines for low-speed vehicles in metropolitan settings, regulations for [...] Read more.
As autonomous vehicle (AV) capabilities expand, it is important to ensure their safety during testing and deployment for public usage. While several testing regulations have been proposed in research, US federal, and even global guidelines for low-speed vehicles in metropolitan settings, regulations for high-speed travel are mainly vacant—this is especially true for regulations relating to AV emergency obstacle avoidance maneuvers (EOAMs). Research in this manuscript introduces a general obstacle avoidance capability assessment (GOACA) for AVs traveling at highway speeds. This GOACA includes test modes including car and bicycle active road objects (AROs) in rural and urban highway settings. These tests were novel in their definitions, methodologies, and execution, especially in the context of AVs driving at highway speeds—critically, this research proposes a test evaluation protocol such that it could serve as a foundation for an official regulation in the future. These GOACA tests included adjacent traffic vehicles which have not been utilized in the prior literature when considering EOAMs within a wholistic AV system context. While the vehicle type will cause simulation results to var, in general, vehicle-to-vehicle (V2V) communication is recommended for usage with AVs at highway speeds for critical safety. This is especially true when considering oncoming traffic and low surface μ conditions. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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40 pages, 2489 KB  
Article
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behavior
by Daniel Flores-Martin, Sergio Laso and Juan Luis Herrera
Electronics 2024, 13(24), 4897; https://doi.org/10.3390/electronics13244897 - 12 Dec 2024
Cited by 1 | Viewed by 6944
Abstract
Smartphones have become a central element in modern society with their widespread adoption driven by technological advancements and their ability to facilitate everyday tasks. A critical feature influencing user satisfaction and smartphone adoption is battery life, as the intensive use of mobile devices [...] Read more.
Smartphones have become a central element in modern society with their widespread adoption driven by technological advancements and their ability to facilitate everyday tasks. A critical feature influencing user satisfaction and smartphone adoption is battery life, as the intensive use of mobile devices can significantly drain battery power. This paper addresses the challenge of predicting smartphone battery consumption using artificial intelligence techniques, specifically deep learning, to optimize energy efficiency. By collecting and analyzing data from mobile devices, such as application usage, screen time, network type, network usage, and battery temperature among others, we developed a predictive model tailored to user-specific behavior. This model identifies the key variables affecting battery consumption and provides personalized energy-saving strategies. Our approach offers a solution for improving battery performance, contributing to more efficient energy management in both hardware and networking terms while adapting to individual usage patterns. The results demonstrate that our approach can significantly predict the battery to anticipate power demands based on user-specific usage. While challenges remain, such as improving the generalizability of the model across different devices, this approach provides a scalable and adaptive method to improve the energy efficiency of smartphones, which will allow efficient management solutions to be suggested, contributing to better battery and network management to improve user experience and device longevity. Full article
(This article belongs to the Special Issue Ubiquitous Computing and Mobile Computing)
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