Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Control and Systems Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Folded Narrow-Band and Wide-Band Monopole Antennas with In-Plane and Vertical Grounds for Wireless Sensor Nodes in Smart Home IoT Applications
Electronics 2024, 13(12), 2262; https://doi.org/10.3390/electronics13122262 (registering DOI) - 8 Jun 2024
Abstract
This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as
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This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as the wireless remote control of house appliances. Two different antennas are proposed to cover the RFID bands of North America (902–928 MHz) and worldwide (860–960 MHz). The antennas have wide horizontal radiation patterns that provide great reading coverage in their communication with a base station placed at a certain distance from the antennas. The structures have two ground planes, one in-plane and the other vertical. The vertical ground helps the antenna to have a directive radiation and also makes it easily installed on walls. The antenna feeding line lies over the vertical ground substrate. The maximum dimensions of the narrow-band antenna are L × W = 0.3 0.14 , and those for the wide-band antenna are L × W = 0.39 0.14 . The measured results show that the bandwidth of the proposed antennas for the North America and worldwide RFID bands are from 902 MHz to 939 MHz and 822 MHz to 961 MHz, with maximum gains of 4.2 dBi and 4.9 dBi, respectively.
Full article
(This article belongs to the Special Issue Antenna Design and Its Applications)
Open AccessArticle
Analysis of Vulnerabilities in College Web-Based System
by
Younsu Nam and Sunoh Choi
Electronics 2024, 13(12), 2261; https://doi.org/10.3390/electronics13122261 (registering DOI) - 8 Jun 2024
Abstract
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Web-based systems are used extensively in Korea because web standards have been adapted by the law (e.g., Electronic Government Act). Users can easily access web-based systems if they are connected to the Internet. However, distinguishing between malicious and benign access is very difficult
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Web-based systems are used extensively in Korea because web standards have been adapted by the law (e.g., Electronic Government Act). Users can easily access web-based systems if they are connected to the Internet. However, distinguishing between malicious and benign access is very difficult and many potential vulnerabilities exist. In this study, we attempt to leak the information of other users without permission using a non-encrypted API and web source code analysis in a college web-based system. An experiment demonstrates that the information (e.g., other students’ course grades) can be leaked and abnormal data can be embedded in the request. In addition, we discuss methods for preventing such vulnerability attacks.
Full article
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Open AccessArticle
SPICE-Compatible Circuit Models of Multiports Described by Scattering Parameters with Arbitrary Reference Impedances
by
Marek Nałęcz
Electronics 2024, 13(12), 2260; https://doi.org/10.3390/electronics13122260 (registering DOI) - 8 Jun 2024
Abstract
New SPICE-compatible circuit models of a multiport are presented here that are suitable for the frequency-domain and time-domain analyses of hybrid systems containing linear distributed elements and possibly non-linear lumped elements. Distributed elements models are based on scattering parameters with potentially complex reference
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New SPICE-compatible circuit models of a multiport are presented here that are suitable for the frequency-domain and time-domain analyses of hybrid systems containing linear distributed elements and possibly non-linear lumped elements. Distributed elements models are based on scattering parameters with potentially complex reference impedances, which are not necessarily equal for all ports. Both exact and approximated (lumped) models are proposed. The scattering parameters are directly taken as the model element values in the former case. In the latter case, the model element values are equal to the real and imaginary parts of the poles and residues of the rational approximation. The models comprise a multiport (with an admittance matrix numerically equal to the modeled scattering matrix or approximating it) equipped with a pair of coupled impedances at each port. A few examples validate the proposed approach and prove its efficiency in terms of matrix size and analysis time compared to some selected commercial counterparts.
Full article
(This article belongs to the Section Microwave and Wireless Communications)
Open AccessReview
An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies
by
Anny Huaman-Rivera, Ricardo Calloquispe-Huallpa, Adriana C. Luna Hernandez and Agustin Irizarry-Rivera
Electronics 2024, 13(12), 2259; https://doi.org/10.3390/electronics13122259 (registering DOI) - 8 Jun 2024
Abstract
The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges
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The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.
Full article
(This article belongs to the Special Issue Power Electronics and Its Applications in Power System)
Open AccessEditorial
Digital Twins in Industry 4.0
by
Sangchan Park, Sira Maliphol, Jiyoung Woo and Liu Fan
Electronics 2024, 13(12), 2258; https://doi.org/10.3390/electronics13122258 (registering DOI) - 8 Jun 2024
Abstract
Since Grieves [...]
Full article
(This article belongs to the Special Issue Digital Twins in Industry 4.0)
Open AccessArticle
A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion
by
Yang Xu, Yonghua Xia, Quai Zhao, Kaihua Yang and Qiang Li
Electronics 2024, 13(12), 2257; https://doi.org/10.3390/electronics13122257 (registering DOI) - 8 Jun 2024
Abstract
To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous
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To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.
Full article
(This article belongs to the Special Issue Computer Vision for Modern Vehicles)
Open AccessArticle
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Machine Learning
by
Jian Yu, Qiang Li and Lei Li
Electronics 2024, 13(12), 2256; https://doi.org/10.3390/electronics13122256 (registering DOI) - 8 Jun 2024
Abstract
Coordinated cyber-physical attacks (CCPAs) are dangerously stealthy and have considerable destructive effects against power grids. The problem of stealthy CCPA (SCCPA) localization, specifically identifying disconnected lines in attack, is a nonlinear multi-classification problem. To the best of our knowledge, only one paper has
[...] Read more.
Coordinated cyber-physical attacks (CCPAs) are dangerously stealthy and have considerable destructive effects against power grids. The problem of stealthy CCPA (SCCPA) localization, specifically identifying disconnected lines in attack, is a nonlinear multi-classification problem. To the best of our knowledge, only one paper has studied the problem; nevertheless, the total number of classifications is not appropriate. In the paper, we propose several methods to solve the problem of SCCPA localization. Firstly, considering the practical constraints and abiding by one of our previous studies, we elaborately determine the total number of classifications and design an approach for generating training and testing datasets. Secondly, we develop two algorithms to solve multiple classifications via the support vector machine (SVM) and random forest (RF), respectively. Similarly, we also present a one-dimensional convolutional neural network (1D-CNN) architecture. Finally, extensive simulations are carried out for IEEE 14-bus, 30-bus, and 118-bus power system, respectively, and we verify the effectiveness of our approaches in solving the problem of SCCPA localization.
Full article
(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)
Open AccessArticle
Improving Training Dataset Balance with ChatGPT Prompt Engineering
by
Mateusz Kochanek, Igor Cichecki, Oliwier Kaszyca, Dominika Szydło, Michał Madej, Dawid Jędrzejewski, Przemysław Kazienko and Jan Kocoń
Electronics 2024, 13(12), 2255; https://doi.org/10.3390/electronics13122255 (registering DOI) - 8 Jun 2024
Abstract
The rapid evolution of large language models, in particular OpenAI’s GPT-3.5-turbo and GPT-4, indicates a growing interest in advanced computational methodologies. This paper proposes a novel approach to synthetic data generation and knowledge distillation through prompt engineering. The potential of large language models
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The rapid evolution of large language models, in particular OpenAI’s GPT-3.5-turbo and GPT-4, indicates a growing interest in advanced computational methodologies. This paper proposes a novel approach to synthetic data generation and knowledge distillation through prompt engineering. The potential of large language models (LLMs) is used to address the problem of unbalanced training datasets for other machine learning models. This is not only a common issue but also a crucial determinant of the final model quality and performance. Three prompting strategies have been considered: basic, composite, and similarity prompts. Although the initial results do not match the performance of comprehensive datasets, the similarity prompts method exhibits considerable promise, thus outperforming other methods. The investigation of our rebalancing methods opens pathways for future research on leveraging continuously developed LLMs for the enhanced generation of high-quality synthetic data. This could have an impact on many large-scale engineering applications.
Full article
(This article belongs to the Special Issue Advances in Large Language Model Empowered Machine Learning: Design and Application)
Open AccessBrief Report
A Simple Scan Driver Circuit Suitable for Depletion-Mode Metal-Oxide Thin-Film Transistors in Active-Matrix Displays
by
Yikyoung You, Junhyung Lim, Kyoungseok Son, Jaybum Kim, Youngoo Kim, Kyunghoe Lee, Kyunghoon Chung and Keechan Park
Electronics 2024, 13(12), 2254; https://doi.org/10.3390/electronics13122254 (registering DOI) - 8 Jun 2024
Abstract
Metal-oxide (MOx) thin-film transistors (TFTs) require complex circuit structures to cope with their depletion mode characteristics, making them applicable only to large-area active matrix (AM) displays despite their low manufacturing cost and decent performance. In this paper, we report a simple MOx 10T-2C
[...] Read more.
Metal-oxide (MOx) thin-film transistors (TFTs) require complex circuit structures to cope with their depletion mode characteristics, making them applicable only to large-area active matrix (AM) displays despite their low manufacturing cost and decent performance. In this paper, we report a simple MOx 10T-2C scan driver circuit that overcomes the depletion mode characteristics using a series-connected two transistor (STT) configuration and clock signals with two kinds of low-voltage levels. The proposed circuit has a wide operating range of TFT characteristics, i.e., −2.8 V ≤ VTH ≤ +3.0 V. Through the measurement results of the manufactured sample, it was confirmed that the performance and area of our circuit are suitable for high-resolution mobile displays.
Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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Open AccessSystematic Review
Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review
by
Eleni Mitsea, Athanasios Drigas and Charalabos Skianis
Electronics 2024, 13(12), 2253; https://doi.org/10.3390/electronics13122253 (registering DOI) - 8 Jun 2024
Abstract
Breathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize
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Breathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize breathing as a tool for self-regulation. After the COVID-19 pandemic, a global discussion has begun about the potential role of emerging technologies in breath-control interventions. Emerging technologies refer to a wide range of advanced technologies that have already entered the race for mental health training. Artificial intelligence, immersive technologies, biofeedback, non-invasive neurofeedback, and other wearable devices provide new, but yet underexplored, opportunities in breathing training. Thus, the current systematic review examines the synergy between emerging technologies and breathing techniques for improving mental and emotional health through the lens of skills development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized to respond to the objectives and research questions. The potential benefits, possible risks, ethical concerns, future directions, and implications are also discussed. The results indicated that digitally assisted breathing can improve various aspects of mental health (i.e., attentional control, emotional regulation, mental flexibility, stress management, and self-regulation). A significant finding of this review indicated that the blending of different technologies may maximize training outcomes. Thus, future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations. This study aspires to provide positive feedback in the discussion about the role of digital technologies in assisting mental and emotional health-promoting interventions among populations with different needs (i.e., employees, students, and people with disabilities).
Full article
Open AccessArticle
Design of a Switching Strategy for Output Voltage Tracking Control in a DC-DC Buck Power Converter
by
Eduardo Hernández-Márquez, Panuncio Cruz-Francisco, Eric Hernández-Castillo, Dulce Martinez-Peón, Rafael Castro-Linares, José Rafael García-Sánchez, Alfredo Roldán-Caballero, Xóchitl Siordia-Vásquez and Juan Carlos Valdivia-Corona
Electronics 2024, 13(12), 2252; https://doi.org/10.3390/electronics13122252 (registering DOI) - 8 Jun 2024
Abstract
This work proposes the design of a commutation function to solve the output voltage trajectory tracking problem in the DC-DC Buck power electronic converter. Through a Lyapunov-type analysis, sufficient conditions are established, taking into account the discontinuous model, to ensure asymptotic convergence to
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This work proposes the design of a commutation function to solve the output voltage trajectory tracking problem in the DC-DC Buck power electronic converter. Through a Lyapunov-type analysis, sufficient conditions are established, taking into account the discontinuous model, to ensure asymptotic convergence to the desired trajectories. Based on this analysis, a state-dependent switching function was designed to guarantee the closed-loop stability of the tracking error. To validate the control performance, circuit numerical simulations were carried out under abrupt disturbances in the source and load of the converter. The results demonstrate that the voltage tracking at the output of the converter is satisfactorily achieved.
Full article
(This article belongs to the Special Issue Applications, Control and Design of Power Electronics Converters)
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Open AccessArticle
Textile Antenna with Dual Bands and SAR Measurements for Wearable Communication
by
Mahmoud A. Abdelghany, Mohamed I. Ahmed, Ahmed A. Ibrahim, Arpan Desai and Mai. F. Ahmed
Electronics 2024, 13(12), 2251; https://doi.org/10.3390/electronics13122251 (registering DOI) - 8 Jun 2024
Abstract
A novel dual-wideband textile antenna designed for wearable applications is introduced in this study. Embedding antennas into wearable devices requires a detailed analysis of the specific absorption rate (SAR) to ensure safety. To achieve this, SAR values were meticulously simulated and evaluated within
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A novel dual-wideband textile antenna designed for wearable applications is introduced in this study. Embedding antennas into wearable devices requires a detailed analysis of the specific absorption rate (SAR) to ensure safety. To achieve this, SAR values were meticulously simulated and evaluated within a human voxel model, considering various body regions such as the left/right head and the abdominal region. The proposed antenna is a monopole design utilizing denim textile as the substrate material. The characterization of the denim textile substrate is carried out using two different methods. The first analysis included a DAC (Dielectric Assessment Kit), while a ring resonator technique was employed for the second examination. Operating within the frequency bands of (58.06%) 2.2–4 GHz and (61.43) 5.3–10 GHz, the antenna demonstrated flexibility in its dual-wideband capabilities. Extensive simulations and tests were conducted to assess the performance of the antenna in both flat and bent configurations. The SAR results obtained from these tests indicate that the antenna complies with safety standard limits when integrated with the human voxel model. This validation underscores the potential of the proposed antenna for seamless integration into wearable applications, offering a promising solution for future developments in this domain.
Full article
(This article belongs to the Special Issue Antenna and Propagation Technologies for 5G/6G Communication)
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Open AccessArticle
An Advanced Approach to Object Detection and Tracking in Robotics and Autonomous Vehicles Using YOLOv8 and LiDAR Data Fusion
by
Yanyan Dai, DeokGyu Kim and KiDong Lee
Electronics 2024, 13(12), 2250; https://doi.org/10.3390/electronics13122250 (registering DOI) - 7 Jun 2024
Abstract
Accurately and reliably perceiving the environment is a major challenge in autonomous driving and robotics research. Traditional vision-based methods often suffer from varying lighting conditions, occlusions, and complex environments. This paper addresses these challenges by combining a deep learning-based object detection algorithm, YOLOv8,
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Accurately and reliably perceiving the environment is a major challenge in autonomous driving and robotics research. Traditional vision-based methods often suffer from varying lighting conditions, occlusions, and complex environments. This paper addresses these challenges by combining a deep learning-based object detection algorithm, YOLOv8, with LiDAR data fusion technology. The principle of this combination is to merge the advantages of these technologies: YOLOv8 excels in real-time object detection and classification through RGB images, while LiDAR provides accurate distance measurement and 3D spatial information, regardless of lighting conditions. The integration aims to apply the high accuracy and robustness of YOLOv8 in identifying and classifying objects, as well as the depth data provided by LiDAR. This combination enhances the overall environmental perception, which is critical for the reliability and safety of autonomous systems. However, this fusion brings some research challenges, including data calibration between different sensors, filtering ground points from LiDAR point clouds, and managing the computational complexity of processing large datasets. This paper presents a comprehensive approach to address these challenges. Firstly, a simple algorithm is introduced to filter out ground points from LiDAR point clouds, which are essential for accurate object detection, by setting different threshold heights based on the terrain. Secondly, YOLOv8, trained on a customized dataset, is utilized for object detection in images, generating 2D bounding boxes around detected objects. Thirdly, a calibration algorithm is developed to transform 3D LiDAR coordinates to image pixel coordinates, which are vital for correlating LiDAR data with image-based object detection results. Fourthly, a method for clustering different objects based on the fused data is proposed, followed by an object tracking algorithm to compute the 3D poses of objects and their relative distances from a robot. The Agilex Scout Mini robot, equipped with Velodyne 16-channel LiDAR and an Intel D435 camera, is employed for data collection and experimentation. Finally, the experimental results validate the effectiveness of the proposed algorithms and methods.
Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
Open AccessArticle
Advanced Primary Frequency Regulation Optimization in Wind Storage Systems with DC Integration Using Double Deep Q-Networks
by
Xiaojiang Liu, Peng Zou, Jin You, Yuhong Wang, Jiabao Wu, Zongsheng Zheng, Shilin Gao and Wei Hao
Electronics 2024, 13(12), 2249; https://doi.org/10.3390/electronics13122249 - 7 Jun 2024
Abstract
With the gradual increase in wind power installation capacity, the proportion of traditional synchronous generators driven by fossil fuel is gradually declining. Due to the fact that wind turbines are connected to the grid through power electronic converters, which decouple rotor speeds from
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With the gradual increase in wind power installation capacity, the proportion of traditional synchronous generators driven by fossil fuel is gradually declining. Due to the fact that wind turbines are connected to the grid through power electronic converters, which decouple rotor speeds from the system frequency and reduce system inertia levels, inadequate inertia levels can pose a threat to frequency stability when disturbances occur. To address this issue, this paper proposes a frequency regulation optimization strategy for the direct current (DC) transmission of a wind storage system. This strategy incorporates virtual inertia control and virtual droop control to adjust wind power output based on frequency deviation and rate of change. Fuzzy logic control is employed for energy storage, adaptively adjusting active power based on frequency deviation and the rate of change. Additionally, under the context of multi-DC transmission in renewable energy systems, an optimization strategy for proportion and integration (PI) parameters of the frequency limit controller (FLC) is proposed. Considering frequency deviation and DC regulation power simultaneously, the double deep Q-network (DDQN) algorithm is adopted in the simulation model to attain the optimal parameters of FLC. Simulation results conducted using MATLAB/Simulink 2022a indicate that this strategy increases the lowest frequency by 0.28 Hz and decreases the response time by 1.04 s compared with the non-optimized strategy.
Full article
(This article belongs to the Special Issue Advances in Modeling, Control and Protection of Power System Containing a High Proportion of Power Electronics)
Open AccessArticle
Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures
by
Álvaro Gago-Fabero, Luis Muñoz-Saavedra, Javier Civit-Masot, Francisco Luna-Perejón, José María Rodríguez Corral and Manuel Domínguez-Morales
Electronics 2024, 13(12), 2248; https://doi.org/10.3390/electronics13122248 - 7 Jun 2024
Abstract
Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to
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Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature.
Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
Open AccessArticle
Continuous Recording of Resonator Characteristics Using Single-Sideband Modulation
by
Martin Lippmann, Moritz Hitzemann, Leonardo Hermeling, Kirsten J. Dehning, Jonas Winkelholz, Rene Wantosch and Stefan Zimmermann
Electronics 2024, 13(12), 2247; https://doi.org/10.3390/electronics13122247 - 7 Jun 2024
Abstract
Electrical resonators are usually characterized by their resonance frequency, attenuation and quality factor. External quantities can affect these parameters, resulting in a characteristic change in the resonator, which can be used as a sensor effect. This work presents a new concept and electronic
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Electrical resonators are usually characterized by their resonance frequency, attenuation and quality factor. External quantities can affect these parameters, resulting in a characteristic change in the resonator, which can be used as a sensor effect. This work presents a new concept and electronic device for the continuous recording of resonator characteristics using single-sideband modulation. A test signal consisting of a center frequency and two sidebands is generated and the center frequency is set close to the resonator’s resonance frequency while the two sidebands are adjusted symmetrically around the center frequency. By exiting the resonator with the test signal and demodulating the resulting output into individual frequency components, a continuous measurement of the attenuation is possible. The center frequency is adjusted so that both sidebands have equal attenuation, resulting in a center frequency that corresponds to the resonance frequency of the resonator. If the resonator does not show a symmetrical frequency response, the sideband attenuation ratio can be adjusted accordingly. Continuous recording of the resonator characteristics at a sampling rate of 100 Sps was verified using a digitally tunable RLC series resonator with resonance frequencies between 250 MHz and 450 MHz, resulting in a maximum error below 1.5%.
Full article
(This article belongs to the Special Issue Advances in Signals and Systems Research)
Open AccessArticle
Detecting Logos for Indoor Environmental Perception Using Unsupervised and Few-Shot Learning
by
Changjiang Yin, Qin Ye, Shaoming Zhang and Zexin Yang
Electronics 2024, 13(12), 2246; https://doi.org/10.3390/electronics13122246 - 7 Jun 2024
Abstract
Indoor scenes are crucial components of urban spaces, with logos serving as vital information within these environments. The accurate perception of logos is essential for effectively operating mobile robots in indoor environments, which significantly contributes to many upper-level applications. With the rapid development
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Indoor scenes are crucial components of urban spaces, with logos serving as vital information within these environments. The accurate perception of logos is essential for effectively operating mobile robots in indoor environments, which significantly contributes to many upper-level applications. With the rapid development of neural networks, numerous deep-learning-based object-detection methods have been applied to logo detection. However, most of these methods depend on large labeled datasets. Given the fast-changing nature of logos in indoor scenes, achieving reliable detection performance with either the existing large labeled datasets or a limited number of labeled logos remains challenging. In this article, we propose a method named MobileNetV2-YOLOv4-UP, which integrates unsupervised learning with few-shot learning for logo detection. We develop an autoencoder to obtain latent feature representations of logos by pre-training on a public unlabeled logo dataset. Subsequently, we construct a lightweight logo-detection network and embed the encoder weights as prior information. Training is performed on a small dataset of labeled indoor-scene logos to update the weights of the logo-detection network. Experimental results on the public logo625 dataset and our self-collected LOGO2000 dataset demonstrate that our method outperforms classic object-detection methods, achieving a mean average detection precision of 83.8%. Notably, our unsupervised pre-training strategy (UP) has proven effective, delivering a 15.4% improvement.
Full article
(This article belongs to the Special Issue Object Detection, Segmentation and Categorization in Artificial Intelligence)
Open AccessArticle
Wearable Loops for Dynamic Monitoring of Joint Flexion: A Machine Learning Approach
by
Henry Saltzman, Rahul Rajaram, Yingzhe Zhang, Md Asiful Islam and Asimina Kiourti
Electronics 2024, 13(12), 2245; https://doi.org/10.3390/electronics13122245 - 7 Jun 2024
Abstract
We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of
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We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of the measured angles. We train and validate the ANN for sagittal plane flexion of a leg phantom emulating slow motion, walking, brisk walking, and jogging. We fabricate the loops on conductive threads and evaluate the effect of fabric drift via measurements in the absence and presence of fabric. In the absence of fabric, our model produced a root mean square error (RMSE) of 5.90°, 6.11°, 5.90°, and 5.44° during slow motion, walking, brisk walking, and jogging. The presence of fabric degraded the RMSE to 8.97°, 7.21°, 9.41°, and 7.79°, respectively. Without the proposed ANN method, errors exceeded 35.07° for all scenarios. Proof-of-concept results on three human subjects further validate this performance. Our approach empowers feasibility of wearable loop sensors for motion capture in dynamic, real-world environments. Increasing speed of motion and the presence of fabric degrade sensor performance due to added noise. Nevertheless, the proposed framework is generalizable and can be expanded upon in the future to improve upon the reported angular resolution.
Full article
(This article belongs to the Special Issue Wearable Electronics for Noninvasive Sensing)
Open AccessArticle
The Impact of Grid Distortion on the Power Conversion Harmonics of AC/DC Converters in the Supraharmonic Range
by
Marwa S. Osheba, Abdellatif M. Aboutaleb, Jan Desmet and Jos Knockaert
Electronics 2024, 13(12), 2244; https://doi.org/10.3390/electronics13122244 - 7 Jun 2024
Abstract
AC/DC converters, controlled by pulse width modulation (PWM) and used as power factor correction (PFC), is considered one of the main contributors to emissions in the range 2 kHz–150 kHz, recently known as the supraharmonic (SH) range. This study looks at the impact
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AC/DC converters, controlled by pulse width modulation (PWM) and used as power factor correction (PFC), is considered one of the main contributors to emissions in the range 2 kHz–150 kHz, recently known as the supraharmonic (SH) range. This study looks at the impact of SH grid distortion on the LF (<2 kHz) and HF (>2 kHz) emission of an AC/DC converter. The PFC boost converter is used as a particular case for validation of the results. It is observed that the AC/DC converters emit additional LF interharmonics and subharmonics when the grid voltage contains interharmonic components in the SH range. A mathematical analysis is provided to study and assess the interference between the SH in the background distortion and the AC/DC converters. Experimental studies are then performed for a PFC boost setup based on dSPACE MicroLabBox for the purposes of validating the mathematical analysis.
Full article
(This article belongs to the Special Issue Design and Control of High-Power AC-DC/DC-DC Power Converters in Emerging Energy and Industrial Applications)
Open AccessArticle
An Efficient Transformer–CNN Network for Document Image Binarization
by
Lina Zhang, Kaiyuan Wang and Yi Wan
Electronics 2024, 13(12), 2243; https://doi.org/10.3390/electronics13122243 - 7 Jun 2024
Abstract
Color image binarization plays a pivotal role in image preprocessing work and significantly impacts subsequent tasks, particularly for text recognition. This paper concentrates on document image binarization (DIB), which aims to separate an image into a foreground (text) and background (non-text content). We
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Color image binarization plays a pivotal role in image preprocessing work and significantly impacts subsequent tasks, particularly for text recognition. This paper concentrates on document image binarization (DIB), which aims to separate an image into a foreground (text) and background (non-text content). We thoroughly analyze conventional and deep-learning-based approaches and conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields of pre- and post-network training to underscore the Transformer model’s advantages. Subsequently, we introduce a lightweight model based on the U-Net structure and enhanced with the MobileViT module to capture global information features in document images better. Given its adeptness at learning both local and global features, our proposed model demonstrates competitive performance on two standard datasets (DIBCO2012 and DIBCO2017) and good robustness on the DIBCO2019 dataset. Notably, our proposed method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing, eschewing the use of ensemble models. Moreover, its parameter count is less than one-eighth of the model, which achieves the best results on most DIBCO datasets. Finally, two sets of ablation experiments are conducted to verify the effectiveness of the proposed binarization model.
Full article
(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)
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