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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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26 pages, 1397 KiB  
Article
Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks
by Matteo Cardoni, Danilo Pietro Pau, Kiarash Rezaei and Camilla Mura
Electronics 2024, 13(21), 4278; https://doi.org/10.3390/electronics13214278 - 31 Oct 2024
Viewed by 2740
Abstract
This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision [...] Read more.
This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements. Full article
(This article belongs to the Special Issue Edge Computing and Tiny Machine Learning in the Internet of Things: Latest Advances and Applications)
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34 pages, 9855 KiB  
Article
Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks
by Rana Riad Al-Taie and Xavier Hesselbach
Electronics 2024, 13(21), 4231; https://doi.org/10.3390/electronics13214231 - 29 Oct 2024
Viewed by 1315
Abstract
This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal [...] Read more.
This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments. Full article
(This article belongs to the Special Issue Energy Storage, Analysis and Battery Usage)
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13 pages, 10862 KiB  
Article
Quantum Effects Induced by Defects in Thin-Film Structures: A Hybrid Modeling Approach to Conductance and Transmission Analysis
by Mariusz Mączka, Grzegorz Hałdaś, Stanisław Pawłowski and Ewa Korzeniewska
Electronics 2024, 13(21), 4230; https://doi.org/10.3390/electronics13214230 - 29 Oct 2024
Viewed by 727
Abstract
This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element [...] Read more.
This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element method was combined with a quantum mechanical approach using nonequilibrium Green’s functions, was created. The results of conductance and transmission coefficient simulations for different types of defects in the studied structure and a wide range of temperatures assuming two different control modes are presented. Based on the results, the conditions for the occurrence of quantum effects on the surface of conducting paths containing defects were specified, and their impact on conductance in the quantum mechanical approach was estimated. Full article
(This article belongs to the Section Electronic Materials, Devices and Applications)
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31 pages, 21641 KiB  
Article
A Methodology for the Design of a Compliant Electrocardiograph: A Case Study
by Luis Alberto Gordillo-Roblero, Jorge Alberto Soto-Cajiga, Carlos Romo-Fuentes, Luis Felipe Martínez-Soto and Noé Amir Rodríguez-Olivares
Electronics 2024, 13(21), 4238; https://doi.org/10.3390/electronics13214238 - 29 Oct 2024
Viewed by 1071
Abstract
This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is [...] Read more.
This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is related to the signal’s sampling rate and/or noise levels, and it becomes evident when one signal processing method is intended to work with another, either as a subsequent or simultaneous process. Even though matching algorithms can be implemented to remedy this incompatibility problem, the ultimate solution is the standardization of signals, which depends exclusively on the standardization of hardware. The signal incompatibility problem is urgent to solve because it makes the integration and scalability of different academic works difficult, preventing academia from reaching the stage of development that commercial equipment displays in automatic interpretation procedures. The design methodology presented in this document addresses the stated problem by creating an open-source hardware device capable of acquiring compliant signals, with careful consideration given to Signal Integrity and EMC concepts—a methodology that can be extended to other physiological acquisition systems. The expedited availability of the device’s design documentation and fabrication files is also an advantage of this work. Full article
(This article belongs to the Special Issue Electronic Devices for Bio-Medical Applications)
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15 pages, 1916 KiB  
Article
Charge Transport Characteristics in Doped Organic Semiconductors Using Hall Effect
by Seema Morab, Manickam Minakshi Sundaram and Almantas Pivrikas
Electronics 2024, 13(21), 4223; https://doi.org/10.3390/electronics13214223 - 28 Oct 2024
Cited by 1 | Viewed by 891
Abstract
Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping [...] Read more.
Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping on the performance and reliability of organic semiconductor devices by accurately modeling these processes. In this work, the number density of charge carriers and Hall voltages are computed for n-type doped semiconductors with two different recombination processes, such as non-Langevin and Langevin-type. The findings reveal that in the Langevin system with β′=1, the number density of charge carriers is almost five and four times lower compared with the non-Langevin system with β′=0.01 for increasing dopant concentrations of Npd = 1 and 3, respectively. The Langevin system also had lower Hall voltages than the steady-state and non-Langevin systems for different magnetic fields with dopants, and the non-Langevin system had nearly identical Hall voltages as the steady-state case. The outcome of the current work provides insights into charge transportation mechanisms in low-mobility doped organic semiconductors with Hall effect measurements to improve device efficiency. Full article
(This article belongs to the Section Semiconductor Devices)
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25 pages, 956 KiB  
Article
Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization
by Tsutomu Kumazawa, Munehiro Takimoto, Yasushi Kodama and Yasushi Kambayashi
Electronics 2024, 13(21), 4199; https://doi.org/10.3390/electronics13214199 - 25 Oct 2024
Viewed by 937
Abstract
In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is [...] Read more.
In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality. Full article
(This article belongs to the Special Issue New Advances in Multi-agent Systems: Control and Modelling)
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13 pages, 721 KiB  
Article
Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph
by Jie Song, Baichuan Ren, Yuyu Tang, Jun Wei and Xiaoxian Huang
Electronics 2024, 13(20), 4131; https://doi.org/10.3390/electronics13204131 - 21 Oct 2024
Viewed by 826
Abstract
With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star [...] Read more.
With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level. Full article
(This article belongs to the Section Circuit and Signal Processing)
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16 pages, 8003 KiB  
Article
AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality
by Chaelin Seok, Yeongje Park, Junho Baek, Hyeji Lim, Jong-hyuk Roh, Youngsam Kim, Soohyung Kim and Eui Chul Lee
Electronics 2024, 13(20), 4112; https://doi.org/10.3390/electronics13204112 - 18 Oct 2024
Cited by 3 | Viewed by 990
Abstract
This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This [...] Read more.
This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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18 pages, 7546 KiB  
Article
Measurements of Geometrical Quantities and Selection of Parameters in the Robotic Grinding Process of an Aircraft Engine
by Krzysztof Kurc, Andrzej Burghardt, Magdalena Muszyńska, Paulina Pietruś and Dariusz Szybicki
Electronics 2024, 13(20), 4077; https://doi.org/10.3390/electronics13204077 - 17 Oct 2024
Cited by 1 | Viewed by 3290
Abstract
Aircraft engine blades are produced through various techniques, one of which is precise electrochemical machining (ECM), commonly applied in the aerospace, automotive, and electromechanical industries. This method achieves machining accuracy ranging from 0.1 to 0.3 mm. However, components with complex shapes still require [...] Read more.
Aircraft engine blades are produced through various techniques, one of which is precise electrochemical machining (ECM), commonly applied in the aerospace, automotive, and electromechanical industries. This method achieves machining accuracy ranging from 0.1 to 0.3 mm. However, components with complex shapes still require grinding and polishing. During the grinding of aircraft blades, achieving high precision and maintaining strict dimensional control are essential. This involves monitoring the thickness of the blade at key cross-sections, as well as the radii of the leading and trailing edges, chord lengths, twist angles, and more. The paper introduces a developed robotic blade grinding process featuring iterative laser measurement of geometric parameters. A custom measuring device with laser heads was designed, calibrated, and tested for repeatability. The measurement data were then used to determine the blade feed rate and machining path via a fuzzy logic decision system. The proposed method was validated on a series of PT6 aircraft engine blades in collaboration with Pratt and Whitney Rzeszów. Full article
(This article belongs to the Special Issue Automation, Operation and Maintenance of Control and Communication Systems, 2nd Edition)
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29 pages, 11145 KiB  
Article
Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm
by Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink and Aaron Werth
Electronics 2024, 13(20), 4055; https://doi.org/10.3390/electronics13204055 - 15 Oct 2024
Cited by 1 | Viewed by 1318
Abstract
In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially [...] Read more.
In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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30 pages, 18530 KiB  
Article
Dimensionality Reduction for the Real-Time Light-Field View Synthesis of Kernel-Based Models
by Martijn Courteaux, Hannes Mareen, Bert Ramlot, Peter Lambert and Glenn Van Wallendael
Electronics 2024, 13(20), 4062; https://doi.org/10.3390/electronics13204062 - 15 Oct 2024
Cited by 1 | Viewed by 1179
Abstract
Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which [...] Read more.
Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which is a kernel-based representation. For SMoE to be viable in interactive light-field experiences, real-time view synthesis is crucial yet unsolved. This paper presents two key contributions: a mathematical derivation of a view-specific, intrinsically 2D model from the original 4D light field model and a GPU graphics pipeline that synthesizes these viewpoints in real time. Configuring the proposed GPU implementation for high accuracy, a frequency of 180 to 290 Hz at a resolution of 2048×2048 pixels on an NVIDIA RTX 2080Ti is achieved. Compared to NVIDIA’s instant-ngp Neural Radiance Fields (NeRFs) with the default configuration, our light field rendering technique is 42 to 597 times faster. Additionally, allowing near-imperceptible artifacts in the reconstruction process can further increase speed by 40%. A first-order Taylor approximation causes imperfect views with peak signal-to-noise ratio (PSNR) scores between 45 dB and 63 dB compared to the reference implementation. In conclusion, we present an efficient algorithm for synthesizing 2D views at arbitrary viewpoints from 4D planar light-field SMoE models, enabling real-time, interactive, and high-quality light-field rendering within the SMoE framework. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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18 pages, 10508 KiB  
Article
Magnetic Railway Sleeper Detector
by Lukas Heindler, Harald Hüttmayr, Thomas Thurner and Bernhard Zagar
Electronics 2024, 13(20), 4005; https://doi.org/10.3390/electronics13204005 - 11 Oct 2024
Viewed by 858
Abstract
In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and [...] Read more.
In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios. Full article
(This article belongs to the Special Issue Recent Advances and Applications in New Detectors)
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23 pages, 9813 KiB  
Review
Overview of Reconfigurable Antenna Systems for IoT Devices
by Elena García, Aurora Andújar and Jaume Anguera
Electronics 2024, 13(20), 3988; https://doi.org/10.3390/electronics13203988 - 10 Oct 2024
Viewed by 2789
Abstract
The proliferation of Internet of Things (IoT) devices, such as trackers and sensors, necessitates a delicate balance between device miniaturization and performance. This extends to the antenna system, which must be both efficient and multiband operational while fitting within space-constrained electronic enclosures. Traditional [...] Read more.
The proliferation of Internet of Things (IoT) devices, such as trackers and sensors, necessitates a delicate balance between device miniaturization and performance. This extends to the antenna system, which must be both efficient and multiband operational while fitting within space-constrained electronic enclosures. Traditional antennas, however, struggle to meet these miniaturization demands. Reconfigurable antennas have emerged as a promising solution for adapting their frequency, radiation pattern, or polarization in response to changing requirements, making them ideal for IoT applications. Among various reconfiguration techniques (electrical, mechanical, optical, and material-based), electrical reconfiguration reigns supreme for IoT applications. Its suitability for compact devices, cost-effectiveness, and relative simplicity make it the preferred choice. This paper reviews various approaches to realizing IoT reconfigurable antennas, with a focus on electrical reconfiguration techniques. It categorizes these techniques based on their implementation, including PIN diodes, digital tunable capacitors (DTCs), varactor diodes, and RF switches. It also explores the challenges associated with the development and characterization of IoT reconfigurable antennas, evaluates the strengths and limitations of existing methods, and identifies open challenges for future research. Importantly, the growing trend towards smaller IoT devices has led to the development of antenna boosters. These components, combined with advanced reconfiguration techniques, offer new opportunities for enhancing antenna performance while maintaining a compact footprint. Full article
(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)
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17 pages, 3242 KiB  
Article
Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study
by Kamila Białek, Anna Potulska-Chromik, Jacek Jakubowski, Monika Nojszewska and Anna Kostera-Pruszczyk
Electronics 2024, 13(19), 3962; https://doi.org/10.3390/electronics13193962 - 9 Oct 2024
Viewed by 2219
Abstract
One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using [...] Read more.
One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others). Full article
(This article belongs to the Collection Image and Video Analysis and Understanding)
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12 pages, 5661 KiB  
Article
An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators
by Dong Hee Seo, Jin Woong Lee, Hyuk Mo An and Seok Young Lee
Electronics 2024, 13(19), 3940; https://doi.org/10.3390/electronics13193940 - 5 Oct 2024
Viewed by 1409
Abstract
This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation [...] Read more.
This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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29 pages, 4571 KiB  
Article
Natural Language Inference with Transformer Ensembles and Explainability Techniques
by Isidoros Perikos and Spyro Souli
Electronics 2024, 13(19), 3876; https://doi.org/10.3390/electronics13193876 - 30 Sep 2024
Viewed by 1839
Abstract
Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means [...] Read more.
Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means to illustrate the decision-making procedure of its methods. First, we investigate the performance and generalization capabilities of several transformer-based models, including BERT, ALBERT, RoBERTa, and DeBERTa, across widely used datasets like SNLI, GLUE Benchmark, and ANLI. Then, we employ stacking-ensemble techniques to leverage the strengths of multiple models and improve inference performance. Experimental results demonstrate significant improvements of the ensemble models in inference tasks, highlighting the effectiveness of stacking. Specifically, our best-performing ensemble models surpassed the best-performing individual transformer by 5.31% in accuracy on MNLI-m and MNLI-mm tasks. After that, we implement LIME and SHAP explainability techniques to shed light on the decision-making of the transformer models, indicating how specific words and contextual information are utilized in the transformer inferences procedures. The results indicate that the model properly leverages contextual information and individual words to make decisions but, in some cases, find difficulties in inference scenarios with metaphorical connections which require deeper inferential reasoning. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Engineering)
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13 pages, 10496 KiB  
Article
A Method for Fingerprint Edge Enhancement Based on Radial Hilbert Transform
by Baiyang Wu, Shuo Zhang, Weinan Gao, Yong Bi and Xiaosong Hu
Electronics 2024, 13(19), 3886; https://doi.org/10.3390/electronics13193886 - 30 Sep 2024
Viewed by 850
Abstract
Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using [...] Read more.
Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using the point spread function (PSF) of spiral phase plate (SPP) with a topological charge l=1, for fingerprint edge enhancement. The experimental results show that the processed fingerprint image has more distinct edges, with an increase in information entropy and average gradient. Unlike classical edge detection operators, the fingerprint edge image obtained by the RHLT method exhibits a lower mean square error (MSE) and a higher peak signal-to-noise ratio (PSNR). This indicates that the RHLT method provides more accurate edge detection and demonstrates higher noise-resistance capabilities. Due to its ability to highlight edge information while preserving more original features, this method has great application potential in fingerprint image processing. Full article
(This article belongs to the Section Bioelectronics)
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11 pages, 416 KiB  
Article
Energy Efficiency Evaluation of Artificial Intelligence Algorithms
by Kalin Penev, Alexander Gegov, Olufemi Isiaq and Raheleh Jafari
Electronics 2024, 13(19), 3836; https://doi.org/10.3390/electronics13193836 - 28 Sep 2024
Cited by 1 | Viewed by 2683
Abstract
This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann’s limit on efforts to [...] Read more.
This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann’s limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies. Full article
(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)
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15 pages, 6740 KiB  
Article
Modulation Format Recognition Scheme Based on Discriminant Network in Coherent Optical Communication System
by Fangxu Yang, Qinghua Tian, Xiangjun Xin, Yiqun Pan, Fu Wang, José Antonio Lázaro, Josep M. Fàbrega, Sitong Zhou, Yongjun Wang and Qi Zhang
Electronics 2024, 13(19), 3833; https://doi.org/10.3390/electronics13193833 - 28 Sep 2024
Viewed by 964
Abstract
In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical [...] Read more.
In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical communication. In the one thousand kilometres G.654E optical fiber transmission system, transmission experiments are conducted on the PDM-QPSK/-8PSK/-16QAM/-32QAM/-64QAM modulation format at 8G/16G/32G baud rates, and the signal-to-noise ratio parameters are traversed under experimental conditions. As a key technology in the next-generation elastic optical networks, the modulation format recognition scheme proposed in this paper achieves 100% recognition of the above five modulation formats without distinguishing signal transmission rates. The optical signal-to-noise ratio thresholds required to achieve 100% recognition accuracy are 12.4 dB, 14.3 dB, 15.4 dB, 16.2 dB, and 17.3 dB, respectively. Full article
(This article belongs to the Special Issue Advances in Optical Communication and Optical Computing)
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12 pages, 34840 KiB  
Article
Miniaturized Multiband Substrate-Integrated Waveguide Bandpass Filters with Multi-Layer Configuration and High In-Band Isolation
by Yu Zhan, Yi Wu, Kaixue Ma and Kiat Seng Yeo
Electronics 2024, 13(19), 3834; https://doi.org/10.3390/electronics13193834 - 28 Sep 2024
Cited by 1 | Viewed by 1351
Abstract
This article presents a multiband bandpass filter structure with an in-line topology based on substrate-integrated waveguide (SIW) technology. A multi-layer configuration is employed to achieve circuit miniaturization. By constructing the coupling matrix, the coupling relationships among all resonators are quantitatively characterized, enabling the [...] Read more.
This article presents a multiband bandpass filter structure with an in-line topology based on substrate-integrated waveguide (SIW) technology. A multi-layer configuration is employed to achieve circuit miniaturization. By constructing the coupling matrix, the coupling relationships among all resonators are quantitatively characterized, enabling the extraction of the theoretical frequency response and guiding circuit modeling and optimization. We designed and fabricated a third-order tri-band SIW filter and a third-order quad-band SIW filter, achieving a return loss of nearly 20 dB across all passbands. The close agreement between simulated and measured results validates the proposed design model. Additionally, the high in-band isolation of over 40 dB is demonstrated between all adjacent bands, highlighting the potential applicability of this technology in multiband scenarios. Full article
(This article belongs to the Special Issue RF/MM-Wave/THz Integrated Circuit Design for 5G/6G, Artificial Intelligence, Internet-of-Things, and Future Computing Applications)
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16 pages, 3278 KiB  
Article
Real-Time Wild Horse Crossing Event Detection Using Roadside LiDAR
by Ziru Wang, Hao Xu, Fei Guan and Zhihui Chen
Electronics 2024, 13(19), 3796; https://doi.org/10.3390/electronics13193796 - 25 Sep 2024
Cited by 1 | Viewed by 905
Abstract
Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging [...] Read more.
Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article
(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)
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23 pages, 3964 KiB  
Article
Geometry of Textual Data Augmentation: Insights from Large Language Models
by Sherry J. H. Feng, Edmund M-K. Lai and Weihua Li
Electronics 2024, 13(18), 3781; https://doi.org/10.3390/electronics13183781 - 23 Sep 2024
Cited by 2 | Viewed by 2020
Abstract
Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for [...] Read more.
Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for their effectiveness remains limited. This paper presents a geometric and topological perspective on textual data augmentation using LLMs. We compare the augmentation data generated by GPT-J with those generated through cosine similarity from Word2Vec and GloVe embeddings. Topological data analysis reveals that GPT-J generated data maintains label coherence. Convex hull analysis of such data represented by their two principal components shows that they lie within the spatial boundaries of the original training data. Delaunay triangulation reveals that increasing the number of augmented data points that are connected within these boundaries correlates with improved classification accuracy. These findings provide insights into the superior performance of LLMs in data augmentation. A framework for predicting the usefulness of augmentation data based on geometric properties could be formed based on these techniques. Full article
(This article belongs to the Special Issue Emerging Theory and Applications in Natural Language Processing)
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24 pages, 6207 KiB  
Article
Dynamic Partitioning of Graphs Based on Multivariate Blood Glucose Data—A Graph Neural Network Model for Diabetes Prediction
by Jianjun Li, Xiaozhe Jiang and Kaiyue Wang
Electronics 2024, 13(18), 3727; https://doi.org/10.3390/electronics13183727 - 20 Sep 2024
Cited by 1 | Viewed by 1353
Abstract
Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at [...] Read more.
Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at the node and subgraph levels. This study aimed to evaluate the accuracy of using deep learning (DL) techniques to predict diabetes based on multivariable blood glucose data, aiming to improve resource allocation and decision-making in healthcare. We introduce a Nonlinear Aggregated Graph Neural Network (NLAGNN) that utilizes continuous multivariate historical blood glucose data from multiple patients to predict blood glucose levels over time, addressing the challenge of accurately extracting strong and weak correlation features. We preliminarily propose a Nonlinear Fourier Graph Neural Operator (NFGO) for nonlinear node representation, which effectively reduces meaningless noise. Additionally, a dynamic partitioning of graphs is introduced, which divides the a hypergraph into distinct subgraphs, enabling the further processing of strongly correlated features at the node and subgraph levels, ultimately obtaining the final prediction through layer aggregation. Extensive experiments on three datasets show that our proposed method achieves competitive results compared to existing advanced methods. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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18 pages, 5504 KiB  
Article
Fatigue Driving State Detection Based on Spatial Characteristics of EEG Signals
by Wenwen Chang, Wenchao Nie, Renjie Lv, Lei Zheng, Jialei Lu and Guanghui Yan
Electronics 2024, 13(18), 3742; https://doi.org/10.3390/electronics13183742 - 20 Sep 2024
Cited by 2 | Viewed by 2319
Abstract
Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) [...] Read more.
Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) state and fatigue state by simulating EEG data during simulated driving, this paper proposes a brain functional network construction method based on a phase locking value (PLV) and phase lag index (PLI), studies the relationship between brain regions, and quantitatively analyzes the network structure. The characteristic parameters of the brain functional network that have significant differences in fatigue status are screened out and constitute feature vectors, which are then combined with machine learning algorithms to complete classification and identification. The experimental results show that this method can effectively distinguish between alertness and fatigue states. The recognition accuracy rates of 52 subjects are all above 70%, with the highest recognition accuracy reaching 89.5%. Brain network topology analysis showed that the connectivity between brain regions was weakened under a fatigue state, especially under the PLV method, and the phase synchronization relationship between delta and theta frequency bands was significantly weakened. The research results provide a reference for understanding the interdependence of brain regions under fatigue conditions and the development of fatigue driving detection systems. Full article
(This article belongs to the Section Bioelectronics)
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21 pages, 15716 KiB  
Article
A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies
by Zhanyang Xu, Hong Zhao, Chengxi Xu, Hongyan Shi, Jian Xu and Zhe Wang
Electronics 2024, 13(18), 3710; https://doi.org/10.3390/electronics13183710 - 19 Sep 2024
Viewed by 1649
Abstract
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. [...] Read more.
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems)
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10 pages, 3467 KiB  
Article
Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis
by Emi Yuda, Tomoki Ando, Itaru Kaneko, Yutaka Yoshida and Daisuke Hirahara
Electronics 2024, 13(18), 3671; https://doi.org/10.3390/electronics13183671 - 16 Sep 2024
Viewed by 1536
Abstract
In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original [...] Read more.
In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
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12 pages, 3787 KiB  
Article
Functional Exercise Induces Adaptations in Muscle Oxygen Saturation in Division One Collegiate Butterfly Swimmers: A Randomized Controlled Trial
by Jack Grotke, Austin Alcantara, Joe Amitrano and Dhruv R. Seshadri
Electronics 2024, 13(18), 3680; https://doi.org/10.3390/electronics13183680 - 16 Sep 2024
Cited by 1 | Viewed by 1692
Abstract
This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise [...] Read more.
This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise (EX) and control (CTRL) groups. Weekly 100-yard butterfly sprints were performed, with Muscle Oxygen Saturation (SmO2) continuously monitored using a wearable near-infrared spectroscopy (NIRS) device. This study is among the first to utilize wearable NIRS devices to monitor SmO2 underwater during swimming, demonstrating that a targeted 5-week exercise program significantly improves posterior shoulder endurance, as evidenced by increased Posterior Shoulder Endurance Test (PSET) scores and distinctive SmO2 adaptations in the EX-group compared to the CTRL group. These findings suggest that targeted dryland exercises can enhance posterior shoulder endurance with long-term implications for potentially reducing injury risk and improving performance. Full article
(This article belongs to the Special Issue New Application of Wearable Electronics)
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24 pages, 17247 KiB  
Article
Efficient Lossy Compression of Video Sequences of Automotive High-Dynamic Range Image Sensors for Advanced Driver-Assistance Systems and Autonomous Vehicles
by Paweł Pawłowski and Karol Piniarski
Electronics 2024, 13(18), 3651; https://doi.org/10.3390/electronics13183651 - 13 Sep 2024
Cited by 2 | Viewed by 1482
Abstract
In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive [...] Read more.
In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive industry. However, it offers very low compression rates. To obtain higher compression rates, we suggest using lossy codecs, especially when testing image processing algorithms in software in-the-loop (SiL) or hardware-in-the-loop (HiL) conditions. Our approach leverages the high-quality VP9 codec, operating in two distinct modes: grayscale image compression for automatic image analysis and color (in RGB format) image compression for manual analysis. In both modes, images are acquired from the automotive-specific RCCC (red, clear, clear, clear) image sensor. The codec is designed to achieve a controlled image quality and state-of-the-art compression ratios while maintaining real-time feasibility. In automotive applications, the inherent data loss poses challenges associated with lossy codecs, particularly in rapidly changing scenes with intricate details. To address this, we propose configuring the lossy codecs in variable bitrate (VBR) mode with a constrained quality (CQ) parameter. By adjusting the quantization parameter, users can tailor the codec behavior to their specific application requirements. In this context, a detailed analysis of the quality of lossy compressed images in terms of the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) metrics is presented. With this analysis, we extracted some codec parameters, which have an important impact on preservation of video quality and compression ratio. The proposed compression settings are very efficient: the compression ratios vary from 51 to 7765 for grayscale image mode and from 4.51 to 602.6 for RGB image mode, depending on the specified output image quality settings. We reached 129 frames per second (fps) for compression and 315 fps for decompression in grayscale mode and 102 fps for compression and 121 fps for decompression in the RGB mode. These make it possible to achieve a much higher compression ratio compared to lossless compression while maintaining control over image quality. Full article
(This article belongs to the Special Issue Deep Perception in Autonomous Driving)
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16 pages, 5923 KiB  
Article
Navigating in Light: Precise Indoor Positioning Using Trilateration and Angular Diversity in a Semi-Spherical Photodiode Array with Visible Light Communication
by Javier Barco Alvárez, Juan Carlos Torres Zafra, Juan Sebastián Betancourt, Máximo Morales Cespedes and Carlos Iván del Valle Morales
Electronics 2024, 13(18), 3597; https://doi.org/10.3390/electronics13183597 - 10 Sep 2024
Viewed by 3283
Abstract
This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of [...] Read more.
This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver’s position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system’s potential for significant indoor positioning accuracy and reliability improvements. Full article
(This article belongs to the Special Issue Precision Positioning and Navigation Communication Systems)
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48 pages, 11785 KiB  
Review
State-of-the-Art Electric Vehicle Modeling: Architectures, Control, and Regulations
by Hossam M. Hussein, Ahmed M. Ibrahim, Rawan A. Taha, S. M. Sajjad Hossain Rafin, Mahmoud S. Abdelrahman, Ibtissam Kharchouf and Osama A. Mohammed
Electronics 2024, 13(17), 3578; https://doi.org/10.3390/electronics13173578 - 9 Sep 2024
Cited by 3 | Viewed by 3939
Abstract
The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), [...] Read more.
The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs’ traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector’s commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. Full article
(This article belongs to the Special Issue Featured Review Papers in Electrical and Autonomous Vehicles)
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23 pages, 7018 KiB  
Review
2D and Quasi-2D Halide Perovskite-Based Resistive Switching Memory Systems
by Hyojung Kim, Daijoon Hyun, Muhammad Hilal, Zhicheng Cai and Cheon Woo Moon
Electronics 2024, 13(17), 3572; https://doi.org/10.3390/electronics13173572 - 8 Sep 2024
Cited by 1 | Viewed by 1712
Abstract
Resistive switching (RS) memory devices are gaining recognition as data storage devices due to the significant interest in their switching material, Halide perovskite (HP). The electrical characteristics include hysteresis in its current–voltage (I–V) relationship. It can be attributed to [...] Read more.
Resistive switching (RS) memory devices are gaining recognition as data storage devices due to the significant interest in their switching material, Halide perovskite (HP). The electrical characteristics include hysteresis in its current–voltage (I–V) relationship. It can be attributed to the production and migration of defects. This property allows HPs to be used as RS materials in memory devices. However, 3D HPs are vulnerable to moisture and the surrounding environment, making their devices more susceptible to deterioration. The potential of two-dimensional (2D)/quasi-2D HPs for optoelectronic applications has been recognized, making them a viable alternative to address current restrictions. Two-dimensional/quasi-2D HPs are created by including extended organic cations into the ABX3 frameworks. By adjusting the number of HP layers, it is possible to control the optoelectronic properties to achieve specific features for certain applications. This article presents an overview of 2D/quasi-2D HPs, including their structures, binding energies, and charge transport, compared to 3D HPs. Next, we discuss the operational principles, RS modes (bipolar and unipolar switching), in RS memory devices. Finally, there have been notable and recent breakthroughs in developing RS memory systems using 2D/quasi-2D HPs. Full article
(This article belongs to the Special Issue Advanced Materials for Intelligent Electronics)
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45 pages, 17267 KiB  
Review
Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions
by Tantan Zhang, Haipeng Liu, Weijie Wang and Xinwei Wang
Electronics 2024, 13(17), 3486; https://doi.org/10.3390/electronics13173486 - 2 Sep 2024
Cited by 8 | Viewed by 5620
Abstract
Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios [...] Read more.
Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation. Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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23 pages, 916 KiB  
Article
Fake Base Station Detection and Link Routing Defense
by Sourav Purification, Jinoh Kim, Jonghyun Kim and Sang-Yoon Chang
Electronics 2024, 13(17), 3474; https://doi.org/10.3390/electronics13173474 - 1 Sep 2024
Cited by 1 | Viewed by 2978
Abstract
Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to [...] Read more.
Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station’s threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security. Full article
(This article belongs to the Special Issue Multimedia in Radio Communication and Teleinformatics)
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6 pages, 1827 KiB  
Article
Spike-Timing-Dependent Plasticity Device with Ga-Sn-O Conductance Change Layer Deposited by Mist-CVD Method
by Hidehito Kita, Kazuma Uno, Tokiyoshi Matsuda, Hidenori Kawanishi and Mutsumi Kimura
Electronics 2024, 13(17), 3413; https://doi.org/10.3390/electronics13173413 - 28 Aug 2024
Viewed by 831
Abstract
A spike-timing-dependent plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is [...] Read more.
A spike-timing-dependent plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is an original report on the realization of an STDP characteristic using a thin film deposited by the mist-CVD method, which is achieved by the GTO properties and a well-designed clear methodology to realize a STDP characteristic from a memristive characteristic. Full article
(This article belongs to the Special Issue Feature Papers in Semiconductor Devices)
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17 pages, 3944 KiB  
Article
Comparison of Optimization Methods for the Attitude Control of Satellites
by Ramón Albareda, Karl Stephan Olfe, Álvaro Bello, José Javier Fernández and Victoria Lapuerta
Electronics 2024, 13(17), 3363; https://doi.org/10.3390/electronics13173363 - 24 Aug 2024
Viewed by 1780
Abstract
The definition of multiple operational modes in a satellite is of vital importance for the adaptation of the satellite to the operational demands of the mission and environmental conditions. In this work, three optimization methods were implemented for the initial calibration of an [...] Read more.
The definition of multiple operational modes in a satellite is of vital importance for the adaptation of the satellite to the operational demands of the mission and environmental conditions. In this work, three optimization methods were implemented for the initial calibration of an attitude controller based on fuzzy logic with the purpose of performing an initial exploration of optimal regions of the design space: a multi-objective genetic algorithm (GAMULTIOBJ), a particle swarm optimization (PSO), and a multi-objective particle swarm optimization (MOPSO). The performance of the optimizers was compared in terms of energy cost, accuracy, computational cost, and convergence capabilities of each algorithm. The results show that the PSO algorithm demonstrated superior computational efficiency compared to the others. Concerning the exploration of optimum regions, all algorithms exhibited similar exploratory capabilities. PSO’s low computational cost allowed for thorough scanning of specific interest regions, making it ideal for detailed exploration, whereas MOPSO and GAMULTIOBJ provided more balanced performance with constrained Pareto front elements. Full article
(This article belongs to the Section Systems & Control Engineering)
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33 pages, 4365 KiB  
Article
A Review of Multifunctional Antenna Designs for Internet of Things
by Dimitrios G. Arnaoutoglou, Tzichat M. Empliouk, Theodoros N. F. Kaifas, Michael T. Chryssomallis and George Kyriacou
Electronics 2024, 13(16), 3200; https://doi.org/10.3390/electronics13163200 - 13 Aug 2024
Cited by 7 | Viewed by 2837
Abstract
The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily [...] Read more.
The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into “smart” devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G’s high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices. Full article
(This article belongs to the Special Issue Antenna Designs for 5G/IoT and Space Applications, 2nd Edition)
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21 pages, 6541 KiB  
Article
Comparison of Machine Learning Models for Predicting Interstitial Glucose Using Smart Watch and Food Log
by Haider Ali, Imran Khan Niazi, David White, Malik Naveed Akhter and Samaneh Madanian
Electronics 2024, 13(16), 3192; https://doi.org/10.3390/electronics13163192 - 12 Aug 2024
Cited by 1 | Viewed by 2232
Abstract
This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from [...] Read more.
This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Applications)
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16 pages, 714 KiB  
Article
VN-MADDPG: A Variable-Noise-Based Multi-Agent Reinforcement Learning Algorithm for Autonomous Vehicles at Unsignalized Intersections
by Hao Zhang, Yu Du, Shixin Zhao, Ying Yuan and Qiuqi Gao
Electronics 2024, 13(16), 3180; https://doi.org/10.3390/electronics13163180 - 11 Aug 2024
Cited by 1 | Viewed by 1881
Abstract
The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The [...] Read more.
The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model’s learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Autonomous Driving)
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10 pages, 3114 KiB  
Article
Electrical Characterization of a Unimorph Vibration Energy Harvester with Al/AlN/Al Structure Realized by Magnetron Sputtering
by Daniele Desideri and Federico Moro
Electronics 2024, 13(16), 3135; https://doi.org/10.3390/electronics13163135 - 7 Aug 2024
Viewed by 1323
Abstract
In this work, the realization of a unimorph vibration energy harvester with an Al/AlN/Al structure by magnetron sputtering is proposed. Starting from an Al substrate, the device with an Al/AlN/Al structure was obtained by using a magnetron sputtering in two different operative conditions. [...] Read more.
In this work, the realization of a unimorph vibration energy harvester with an Al/AlN/Al structure by magnetron sputtering is proposed. Starting from an Al substrate, the device with an Al/AlN/Al structure was obtained by using a magnetron sputtering in two different operative conditions. The realized energy harvester was investigated in the unimorph bender set-up. The electrical characterization was performed by estimation of the AlN d31 piezoelectric coefficient and measurements of the output power. The estimated absolute value of d31 was 0.48 pC/N and the maximum output power was about 17 μW with 9.81 m/s2 (rms value) excitation acceleration. Full article
(This article belongs to the Special Issue Micro Energy Harvesters: Modelling, Design, and Applications)
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6 pages, 157 KiB  
Editorial
Special Issue: Recent Advances in Intelligent Vehicular Networks and Communications
by Zhiyuan Ren and Chen Chen
Electronics 2024, 13(15), 3096; https://doi.org/10.3390/electronics13153096 - 5 Aug 2024
Cited by 2 | Viewed by 1861
Abstract
Over the last few decades, research on intelligent vehicular networks and communications has grown significantly due to the increasing demand for advanced vehicular technologies and the need for enhanced road safety, traffic management, and efficient transportation systems [...] Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
16 pages, 482 KiB  
Article
You Are Not Alone! Care Professionals’ Acceptance of Telemedicine in Nursing Homes Comparing Pre- and Post-Implementation Evaluations
by Julia Offermann, Optimal@NRW Research Group and Martina Ziefle
Electronics 2024, 13(15), 3022; https://doi.org/10.3390/electronics13153022 - 31 Jul 2024
Viewed by 1113
Abstract
A lack of personnel in care institutions and high proportions of older people in need of care pose central challenges for today’s aging society, often resulting in the hospitalization of geriatric patients. In many cases, these hospitalizations are not medically necessary and cause [...] Read more.
A lack of personnel in care institutions and high proportions of older people in need of care pose central challenges for today’s aging society, often resulting in the hospitalization of geriatric patients. In many cases, these hospitalizations are not medically necessary and cause deterioration of health. Applying telemedicine in nursing homes represents one approach aimed at a reduction of unnecessary hospitalizations of geriatric patients and supporting care personnel in medically uncertain situations. For a sustainable and successful implementation of technical innovations such as telemedical consultations, the care personnel’s perspectives and acceptance are especially essential. The Optimal@NRW project implemented telemedical consultations in 24 nursing homes in Germany, investigating medical and economic efficiency and in particular also the social acceptance of digital care in nursing homes. This paper presents quantitative results comparing the acceptance evaluations before (PRE: N = 130) and after (POST: N = 87) the implementation of the telemedical consultations in the nursing homes from the perspective of care professionals. The results showed positive evaluations of the telemedical consultations in both evaluation phases: POST evaluations especially showed a lower evaluation of perceived barriers of using telemedical consultations in nursing homes. This study’s insights enable one to derive guidelines and recommendations regarding the communication and information of telemedical applications considering the needs and wishes of care personnel as a central user group. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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19 pages, 2531 KiB  
Article
Determining the Perception Created by Health Warnings on Plain Cigarette Packs with Visual Attention: Eye-Tracking Technique
by Adem Korkmaz, Sevinc Gülsecen, Selahattin Kosunalp and Grigor Mihaylov
Electronics 2024, 13(15), 3000; https://doi.org/10.3390/electronics13153000 - 30 Jul 2024
Cited by 1 | Viewed by 1263
Abstract
This study examines the effects of the plain packaging policy implemented in Türkiye, analyzing how different demographic groups perceive health warnings on cigarette packaging. Employing advanced eye-tracking technology, the research identifies distinct visual attention patterns between smokers and non-smokers when exposed to ‘Anxiety’ [...] Read more.
This study examines the effects of the plain packaging policy implemented in Türkiye, analyzing how different demographic groups perceive health warnings on cigarette packaging. Employing advanced eye-tracking technology, the research identifies distinct visual attention patterns between smokers and non-smokers when exposed to ‘Anxiety’ and ‘Disturbing’ visual cues. Detailed metrics, including fixation counts, durations, and saccade amplitudes, are used to measure and analyze the responses of these groups to the health warnings. The findings reveal that non-smokers significantly focus more on textual warnings, suggesting that text-based elements are more impactful for this group. Conversely, smokers tend to either avoid or become desensitized to disturbing imagery. Additionally, the study finds that female participants exhibit higher saccade amplitudes compared to males, indicating a more thorough examination of the packaging. This gender-specific difference is especially pronounced in their responses to ‘Disturbing’ images, where females show greater engagement, pointing to an increased sensitivity to such stimuli. These insights not only advance our understanding of effective health communication but also underscore the importance of designing public health interventions that cater to the unique responses of different demographic groups. This research significantly enriches the field of tobacco control, providing evidence-based strategies to enhance the effectiveness of visual warnings, thereby supporting targeted smoking cessation efforts. Full article
(This article belongs to the Section Bioelectronics)
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11 pages, 14388 KiB  
Article
Investigation of Defect Formation in Monolithic Integrated GaP Islands on Si Nanotip Wafers
by Ines Häusler, Rostislav Řepa, Adnan Hammud, Oliver Skibitzki and Fariba Hatami
Electronics 2024, 13(15), 2945; https://doi.org/10.3390/electronics13152945 - 26 Jul 2024
Viewed by 1051
Abstract
The monolithic integration of gallium phosphide (GaP), with its green band gap, high refractive index, large optical non-linearity, and broad transmission range on silicon (Si) substrates, is crucial for Si-based optoelectronics and integrated photonics. However, material mismatches, including thermal expansion mismatch and polar/non-polar [...] Read more.
The monolithic integration of gallium phosphide (GaP), with its green band gap, high refractive index, large optical non-linearity, and broad transmission range on silicon (Si) substrates, is crucial for Si-based optoelectronics and integrated photonics. However, material mismatches, including thermal expansion mismatch and polar/non-polar interfaces, cause defects such as stacking faults, microtwins, and anti-phase domains in GaP, adversely affecting its electronic properties. Our paper presents a structural and defect analysis using scanning transmission electron microscopy, high-resolution transmission electron microscopy, and scanning nanobeam electron diffraction of epitaxial GaP islands grown on Si nanotips embedded in SiO2. The Si nanotips were fabricated on 200 mm n-type Si (001) wafers using a CMOS-compatible pilot line, and GaP islands were grown selectively on the tips via gas-source molecular-beam epitaxy. Two sets of samples were investigated: GaP islands nucleated on open Si nanotips and islands nucleated within self-organized nanocavities on top of the nanotips. Our results reveal that in both cases, the GaP islands align with the Si lattice without dislocations due to lattice mismatch. Defects in GaP islands are limited to microtwins and stacking faults. When GaP nucleates in the nanocavities, most defects are trapped, resulting in defect-free GaP islands. Our findings demonstrate an effective approach to mitigate defects in epitaxial GaP on Si nanotip wafers fabricated by CMOS-compatible processes. Full article
(This article belongs to the Special Issue Section Collection Series: Recent Advances in Optoelectronics from Lab to Industry)
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18 pages, 4094 KiB  
Article
Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras
by Danesh Shokri, Christian Larouche and Saeid Homayouni
Electronics 2024, 13(14), 2883; https://doi.org/10.3390/electronics13142883 - 22 Jul 2024
Cited by 3 | Viewed by 1661
Abstract
In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring [...] Read more.
In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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21 pages, 5101 KiB  
Article
Enhancing Scalability of C-V2X and DSRC Vehicular Communication Protocols with LoRa 2.4 GHz in the Scenario of Urban Traffic Systems
by Eduard Zadobrischi and Ștefan Havriliuc
Electronics 2024, 13(14), 2845; https://doi.org/10.3390/electronics13142845 - 19 Jul 2024
Cited by 5 | Viewed by 3058
Abstract
In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration [...] Read more.
In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 2nd Edition)
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12 pages, 2643 KiB  
Article
Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking
by Ji-Hoon Kwon, Hyeong-Jun Kim and Suk Lee
Electronics 2024, 13(14), 2837; https://doi.org/10.3390/electronics13142837 - 18 Jul 2024
Cited by 2 | Viewed by 1495
Abstract
This study investigates the optimization of traffic scheduling in autonomous vehicle networks using time-sensitive networking (TSN), a type of deterministic Ethernet. Ethernet has high bandwidth and compatibility to support various protocols, and its application range is expanding from office environments to smart factories, [...] Read more.
This study investigates the optimization of traffic scheduling in autonomous vehicle networks using time-sensitive networking (TSN), a type of deterministic Ethernet. Ethernet has high bandwidth and compatibility to support various protocols, and its application range is expanding from office environments to smart factories, aerospace, and automobiles. TSN is a representative technology of deterministic Ethernet and is composed of various standards such as time synchronization, stream reservation, seamless redundancy, frame preemption, and scheduled traffic, which are sub-standards of IEEE 802.1 Ethernet established by the IEEE TSN task group. In order to ensure real-time transmission by minimizing end-to-end delay in a TSN network environment, it is necessary to schedule transmission timing in all links transmitting ST (Scheduled Traffic). This paper proposes network performance metrics and methods for applying machine learning (ML) techniques to optimize traffic scheduling. This study demonstrates that the traffic scheduling problem, which has NP-hard complexity, can be optimized using ML algorithms. The performance of each algorithm is compared and analyzed to identify the scheduling algorithm that best meets the network requirements. Reinforcement learning algorithms, specifically DQN (Deep Q Network) and A2C (Advantage Actor-Critic) were used, and normalized performance metrics (E2E delay, jitter, and guard band bandwidth usage) along with an evaluation function based on their weighted sum were proposed. The performance of each algorithm was evaluated using the topology of a real autonomous vehicle network, and their strengths and weaknesses were compared. The results confirm that artificial intelligence-based algorithms are effective for optimizing TSN traffic scheduling. This study suggests that further theoretical and practical research is needed to enhance the feasibility of applying deterministic Ethernet to autonomous vehicle networks, focusing on time synchronization and schedule optimization. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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9 pages, 3014 KiB  
Article
Effects of a Spike-Annealed HfO2 Gate Dielectric Layer on the On-Resistance and Interface Quality of AlGaN/GaN High-Electron-Mobility Transistors
by Gyuhyung Lee, Jeongyong Yang, Min Jae Yeom, Sisung Yoon and Geonwook Yoo
Electronics 2024, 13(14), 2783; https://doi.org/10.3390/electronics13142783 - 15 Jul 2024
Cited by 1 | Viewed by 1566
Abstract
Various high-k dielectrics have been proposed for AlGaN/GaN MOSHEMTs for gate leakage and drain-current collapse suppression. Hafnium oxide (HfO2) is particularly interesting because of its large bandgap, high dielectric constant, and ferroelectricity under specific phase and doping conditions. However, defects and [...] Read more.
Various high-k dielectrics have been proposed for AlGaN/GaN MOSHEMTs for gate leakage and drain-current collapse suppression. Hafnium oxide (HfO2) is particularly interesting because of its large bandgap, high dielectric constant, and ferroelectricity under specific phase and doping conditions. However, defects and surface scattering caused by HfO2 dissimilarity and degraded HfO2/GaN interface quality still leave the challenge of reducing the SS and Ron. In this study, we investigated the effects of the first spike-annealed HfO2 (6 nm) layer, compared with the conventional ALD-HfO2 (6 nm) layer in the HfO2 bilayer gate dielectric structure on AlGaN/GaN HEMTs. Both devices exhibit negligible hysteresis and near-ideal (~60 mV/dec) subthreshold slopes of more than three orders of magnitude. The device with the first annealed HfO2 layer exhibited a reduced Ron with notably less gate bias dependency and enhanced output current. On the other hand, the capacitance–voltage and conductance methods revealed that the border and interface trap densities of the device were inferior to those of the conventional HfO2 layer. The trade-off between enhanced electrical performance and oxide traps is discussed based on these results. Full article
(This article belongs to the Special Issue Challenges, Innovation and Future Perspectives of GaN Technology)
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23 pages, 8556 KiB  
Article
Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios
by Kerui Xia, Jiqing Hu, Zhongnan Wang, Zijian Wang, Zhuo Huang and Zhongchao Liang
Electronics 2024, 13(14), 2773; https://doi.org/10.3390/electronics13142773 - 15 Jul 2024
Cited by 1 | Viewed by 1803
Abstract
With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and [...] Read more.
With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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18 pages, 15489 KiB  
Article
Ultra-Broadband Minuscule Polarization Beam Splitter Based on Dual-Core Photonic Crystal Fiber with Two Silver Wires
by Yuxiang Ji, Hui Zou, Yuhang Du and Ningyi Wang
Electronics 2024, 13(13), 2675; https://doi.org/10.3390/electronics13132675 - 8 Jul 2024
Viewed by 1169
Abstract
This paper presents a polarizing beam splitter (PBS) based on a hexagonal lattice silver-filled photonic crystal fiber (PCF) with two silver wires, which possesses advantages such as a short splitting length, high extinction ratio (ER), and an ultra-wide bandwidth in commonly used communication [...] Read more.
This paper presents a polarizing beam splitter (PBS) based on a hexagonal lattice silver-filled photonic crystal fiber (PCF) with two silver wires, which possesses advantages such as a short splitting length, high extinction ratio (ER), and an ultra-wide bandwidth in commonly used communication bands. Utilizing the full-vector finite element method (FV-FEM), thorough investigations were conducted on lasers within the wavelength range of 1.1 to 1.9 μm. The PBS demonstrates a working bandwidth of 725 nm (1.14 to 1.865 μm) under an ultra-short splitting length of 55.3 μm, with an ER exceeding 20 dB, covering all bands of O + E + S + C + L + U optical communication, and achieving a maximum ER of 74.65 dB, where the surface plasmon resonance (SPR) effect of silver metal plays a significant role. It not only features an ultra-short splitting length and an ultra-wide splitting bandwidth but also exhibits excellent manufacturing tolerances and anti-interference capabilities. This polarizing beam splitter represents a promising candidate in communication and may find various applications in optical communication. Full article
(This article belongs to the Special Issue Advances in Optical Fibers for Fiber Sensors)
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12 pages, 4096 KiB  
Article
A Geometrically Scalable Lumped Model for Spiral Inductors in Radio Frequency GaN Technology on Silicon
by Simone Spataro, Giuseppina Sapone, Marcello Giuffrida and Egidio Ragonese
Electronics 2024, 13(13), 2665; https://doi.org/10.3390/electronics13132665 - 7 Jul 2024
Cited by 1 | Viewed by 1129
Abstract
This paper presents a lumped scalable model for spiral inductors in a radio frequency (RF) gallium nitride (GaN) technology on silicon substrate. The model has been developed by exploiting electromagnetic (EM) simulations of geometrically scaled spiral inductors. To this aim, the technology substrate, [...] Read more.
This paper presents a lumped scalable model for spiral inductors in a radio frequency (RF) gallium nitride (GaN) technology on silicon substrate. The model has been developed by exploiting electromagnetic (EM) simulations of geometrically scaled spiral inductors. To this aim, the technology substrate, i.e., the metal back-end-of-line along with dielectric and semiconductor layers of the adopted GaN process, has been validated by means of experimental data and then used to define the EM simulator set-up for the spiral inductors. The proposed model adopts a simple π-topology with only seven lumped components and predicts inductor performance in terms of inductance, quality factor (Q-factor) and self-resonance frequency (SRF) for a large range of geometrical parameters of the spiral (i.e., number of turns, metal width, inner diameter). Full article
(This article belongs to the Special Issue Wide-Bandgap Device Application: Devices, Circuits, and Drivers)
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