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 (Electrical and Electronic 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
A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation
Electronics 2024, 13(10), 1964; https://doi.org/10.3390/electronics13101964 - 16 May 2024
Abstract
Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for
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Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.
Full article
(This article belongs to the Special Issue Mentor Program: Smart Controller of Energy Aggregators in Distributed Energy Resources)
Open AccessArticle
Research on Electromagnetic Environment Characteristic Acquisition System for Industrial Chips
by
Yanning Chen, Fang Liu, Jie Gao, Zhaowen Yan and Fuyu Zhao
Electronics 2024, 13(10), 1963; https://doi.org/10.3390/electronics13101963 - 16 May 2024
Abstract
With the system interconnection and intelligence of application scenario equipment, the electromagnetic environment of chips is becoming more and more complex. Problems such as communication interruption and data loss caused by electromagnetic interference often occur. The electromagnetic reliability of chips has become an
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With the system interconnection and intelligence of application scenario equipment, the electromagnetic environment of chips is becoming more and more complex. Problems such as communication interruption and data loss caused by electromagnetic interference often occur. The electromagnetic reliability of chips has become an important index to measure their availability. In order to effectively detect the electromagnetic reliability of industrial chips applied to specific scenarios, it is necessary to measure and analyze the electromagnetic characteristics of the application scenarios, as the boundary conditions of the electromagnetic protection simulation analysis and design of the chip, and to develop Electromagnetic Compatibility (EMC) test items, test limits and test methods suitable for carrying out tests and monitoring on chips. The paper presents an acquisition system, which can complete the collection of transient electromagnetic interference, steady electromagnetic field, temperature, humidity and near-field data. The transient interference measurement frequency range is 300 kHz–500 MHz, with a rising edge of 1.5 ns; the steady-state electromagnetic field measurement frequency ranges from 100 Hz to 3 GHz. By collecting the electromagnetic environmental characteristics of chips and analyzing situations in which chips are prone to interference, protective measures can be implemented.
Full article
Open AccessArticle
Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks
by
Asma Alfardus and Danda B. Rawat
Electronics 2024, 13(10), 1962; https://doi.org/10.3390/electronics13101962 - 16 May 2024
Abstract
In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy.
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In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering involves selecting and extracting relevant features from the data that are useful for detecting anomalies. Deep learning involves using neural networks to learn complex patterns and relationships in the data. Our experiments show that the proposed technique have achieved high accuracy in detecting anomalies and outperforms existing state-of-the-art methods. This technique can be used to enhance the security of IVNs and prevent cyber-attacks that can have serious consequences for drivers and passengers.
Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Smart Cities/From 5G to 6G/Digital Twins)
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Open AccessArticle
High-Resolution Millimeter-Wave Radar for Real-Time Detection and Characterization of High-Speed Objects with Rapid Acceleration Capabilities
by
Yair Richter and Nezah Balal
Electronics 2024, 13(10), 1961; https://doi.org/10.3390/electronics13101961 - 16 May 2024
Abstract
In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of
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In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems.
Full article
(This article belongs to the Special Issue Advances in Terahertz Radiation Sources and Their Applications)
Open AccessArticle
Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API
by
Miguel Morales-Chan, Hector R. Amado-Salvatierra, José Amelio Medina, Roberto Barchino, Rocael Hernández-Rizzardini and António Moreira Teixeira
Electronics 2024, 13(10), 1960; https://doi.org/10.3390/electronics13101960 - 16 May 2024
Abstract
Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge
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Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI.
Full article
(This article belongs to the Special Issue Innovations and Challenges of Higher Education Institutions in the Post-COVID-19 Era)
Open AccessArticle
PDPHE: Personal Data Protection for Trans-Border Transmission Based on Homomorphic Encryption
by
Yan Liu, Changshui Yang, Qiang Liu, Mudi Xu, Chi Zhang, Lihong Cheng and Wenyong Wang
Electronics 2024, 13(10), 1959; https://doi.org/10.3390/electronics13101959 - 16 May 2024
Abstract
In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few
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In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing.
Full article
(This article belongs to the Special Issue Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends)
Open AccessArticle
Development of Grid-Forming and Grid-Following Inverter Control in Microgrid Network Ensuring Grid Stability and Frequency Response
by
V. Vignesh Babu, J. Preetha Roselyn, C. Nithya and Prabha Sundaravadivel
Electronics 2024, 13(10), 1958; https://doi.org/10.3390/electronics13101958 - 16 May 2024
Abstract
This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase
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This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase Lock Loop (PLL) to track the phase angle of the voltages at the PCC and adopts a vector control strategy to adjust the active and reactive currents that are injected into the power grid. The drawback of a GFL inverter is that it lacks the capability to operate independently when the utility grid is down due to outages or disturbances. The proposed grid-forming (GFM) inverter control with a virtual synchronous machine provides inertia to the grid, generates a stable grid-like voltage and frequency and enables the integration of the grid. The proposed system incorporates a battery energy storage system (BESS) which has inherent energy storage capability and is independent of geographical areas. The GFM control includes voltage and frequency control, enhanced islanding and black start capability and the maintenance of the stability of the grid-integrated system. The proposed model is validated under varying irradiance conditions, load switching, grid outages and temporary faults with fault ride-through (FRT) capability, and fast frequency response and stability are achieved. The proposed model is validated under varying irradiance conditions, load switching, grid outages and line faults incorporating fault ride-through capability in GFM-based control. The proposed controller was simulated in a 100 MW solar PV system and 60 MW BESS using the MATLAB/Simulink 2023 tool, and the experimental setup was validated in a 1 kW grid-connected system. The percentage improvement of the system frequency and voltage with FRT-capable GFM control is 69.3% and 70%, respectively, and the percentage improvement is only 3% for system frequency and 52% for grid voltage in the case of an FRT-capable GFL controller. The simulation and experimental results prove that GFM-based inverter control achieves fast frequency response, and grid stability is also ensured.
Full article
(This article belongs to the Special Issue State-of-the-Art Power Electronics Systems)
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Open AccessArticle
Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed Books
by
Woo-Seok Lee and Kang-Sun Choi
Electronics 2024, 13(10), 1957; https://doi.org/10.3390/electronics13101957 - 16 May 2024
Abstract
This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss
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This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss function allows the segmentation network to accurately extract character strokes that can be attributed to the typeface of the movable metal type used for printing. Our method calculates deviation between the boundary of predicted character strokes and the counterpart of the ground-truth strokes. Diverging from traditional Euclidean distance metrics, our approach determines the deviation indirectly utilizing boundary pixel-value difference over a Gaussian-smoothed version of the stroke boundary. This approach helps extract characters with smooth boundaries efficiently. Through experiments, it is confirmed that the proposed method not only smoothens stroke boundaries in character extraction, but also effectively eliminates noise and outliers, significantly improving the clarity and accuracy of the segmentation process.
Full article
(This article belongs to the Special Issue Electronics and Computer Science for Cultural Heritage: Advancements, Preservation, and Applications)
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Open AccessArticle
Gait Pattern Identification Using Gait Features
by
Min-Jung Kim, Ji-Hun Han, Woo-Chul Shin and Youn-Sik Hong
Electronics 2024, 13(10), 1956; https://doi.org/10.3390/electronics13101956 - 16 May 2024
Abstract
Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and
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Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modules were implemented on an Arduino Uno R3 board for gait analysis. Gait cycles were identified based on roll values measured by the accelerometer embedded in the IMU sensor. By superimposing the gait cycles that occurred during the walking period, they could be analyzed using statistical methods. We found that the subjects could be identified using the gait feature points extracted through the statistical modeling process. To validate the feasibility of feature-based gait pattern identification, we constructed various machine learning models and compared the accuracy of their gait pattern identification. Based on this, we also investigated whether there was a significant difference between the gait patterns of people who used cell phones while walking and those who did not.
Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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Open AccessArticle
Design of a New Neuro-Generator with a Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences
by
María de Lourdes Rivas Becerra, Juan José Raygoza Panduro, Susana Ortega Cisneros, Edwin Christian Becerra Álvarez and Jaime David Rios Arrañaga
Electronics 2024, 13(10), 1955; https://doi.org/10.3390/electronics13101955 - 16 May 2024
Abstract
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This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike
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This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems.
Full article
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Open AccessArticle
Evaluation of a Simplified Modeling Approach for SEE Cross-Section Prediction: A Case Study of SEU on 6T SRAM Cells
by
Cleiton M. Marques, Frédéric Wrobel, Ygor Q. Aguiar, Alain Michez, Frédéric Saigné, Jérôme Boch, Luigi Dilillo and Rubén García Alía
Electronics 2024, 13(10), 1954; https://doi.org/10.3390/electronics13101954 - 16 May 2024
Abstract
Electrical models play a crucial role in assessing the radiation sensitivity of devices. However, since they are usually not provided for end users, it is essential to have alternative modeling approaches to optimize circuit design before irradiation tests, and to support the understanding
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Electrical models play a crucial role in assessing the radiation sensitivity of devices. However, since they are usually not provided for end users, it is essential to have alternative modeling approaches to optimize circuit design before irradiation tests, and to support the understanding of post-irradiation data. This work proposes a novel simplified methodology to evaluate the single-event effects (SEEs) cross-section. To validate the proposed approach, we consider the 6T SRAM cell a case study in four technological nodes. The modeling considers layout features and the doping profile, presenting ways to estimate unknown parameters. The accuracy and limitations are determined by comparing our simulations with actual experimental data. The results demonstrated a strong correlation with irradiation data, without requiring any fitting of the simulation results or access to process design kit (PDK) data. This proves that our approach is a reliable method for calculating the single-event upset (SEU) cross-section for heavy-ion irradiation.
Full article
(This article belongs to the Special Issue Advanced Non-Volatile Memory Devices and Systems)
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Open AccessArticle
A CMOS Rectifier with a Wide Dynamic Range Using Switchable Self-Bias Polarity for a Radio Frequency Harvester
by
Boon Chiat Terence Teo, Wu Cong Lim, Navaneethan Venkadasamy, Xian Yang Lim, Chiang Liang Kok and Liter Siek
Electronics 2024, 13(10), 1953; https://doi.org/10.3390/electronics13101953 - 16 May 2024
Abstract
This paper presents a switchable self-bias polarity on the CMOS complementary cross-coupled rectifier to improve the rectifier’s power conversion efficiency (PCE) profile across a wide input power (PIN) dynamic range. This technique achieves this by adaptively switching the polarity of the
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This paper presents a switchable self-bias polarity on the CMOS complementary cross-coupled rectifier to improve the rectifier’s power conversion efficiency (PCE) profile across a wide input power (PIN) dynamic range. This technique achieves this by adaptively switching the polarity of the bias on the n-MOS to overdrive it during low PIN to improve the sensitivity and underdrive it during high PIN to suppress the shoot-through loss and the unnecessary discharge of the coupling capacitor. The popular self-biased p-MOS is also implemented further to reduce the reverse conduction loss during high PIN. The proposed rectifier is fabricated in a 40 nm CMOS process and operates at 900 MHz with a load of 50 kΩ. The proposed rectifier achieved a peak PCE of 72.1% and maintained a 0.8xPCEPEAK across a PIN dynamic range of 11.5 dB.
Full article
(This article belongs to the Section Circuit and Signal Processing)
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Open AccessArticle
Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety
by
Haochong Chen, Xincheng Cao, Levent Guvenc and Bilin Aksun-Guvenc
Electronics 2024, 13(10), 1952; https://doi.org/10.3390/electronics13101952 - 16 May 2024
Abstract
The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent
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The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance.
Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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Open AccessArticle
Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification
by
Nikhil Thapa, Ridip Khanal, Bhuwan Bhattarai and Joonwhoan Lee
Electronics 2024, 13(10), 1951; https://doi.org/10.3390/electronics13101951 - 16 May 2024
Abstract
Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine
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Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively.
Full article
(This article belongs to the Special Issue Revolutionizing Medical Image Analysis with Deep Learning)
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Open AccessCommunication
Transfer Learning with Multi-Sequence MRI for Segmentation of Autosomal Dominant Polycystic Kidney Disease Using U-Net
by
Min-Seok Kwon, Yeon-Soon Jung, Jung-Gu Park and Yeh-Chan Ahn
Electronics 2024, 13(10), 1950; https://doi.org/10.3390/electronics13101950 - 16 May 2024
Abstract
In recent studies, the measurement of total kidney volume, a primary indicator for the diagnosis and treatment of renal diseases, has been advanced through artificial-intelligence-driven automated segmentation. However, the limited quantity of medical data remains a persistent challenge, with its scarcity negatively impacting
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In recent studies, the measurement of total kidney volume, a primary indicator for the diagnosis and treatment of renal diseases, has been advanced through artificial-intelligence-driven automated segmentation. However, the limited quantity of medical data remains a persistent challenge, with its scarcity negatively impacting the outcomes of machine learning algorithms. In this study, we have enhanced the accuracy of machine learning for disease diagnosis by employing various MRI sequences commonly used during renal imaging. We created a model for kidney segmentation using U-Net and performed single training, joint training, and transfer learning using MRI images from two sequences based on SSFP and SSFSE. Ultimately, during transfer learning, we achieved the highest accuracy with a Dice coefficient of 0.951 and a mean difference of 2.05% (−3.47%, 7.57%) in Bland–Altman analysis for SSFP. Similarly, for SSFSE, we obtained a Dice coefficient of 0.952 and a mean difference of 4.33% (−7.05%, 15.71%) through Bland–Altman analysis. This demonstrates our ability to enhance prediction accuracy for each sequence by leveraging different sequences.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Image and Video Processing)
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Open AccessArticle
Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration
by
Eneko Artetxe, Oscar Barambones, Imanol Martín Toral, Jokin Uralde, Isidro Calvo and Asier del Rio
Electronics 2024, 13(10), 1949; https://doi.org/10.3390/electronics13101949 - 16 May 2024
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Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes
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Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.
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Open AccessArticle
A Cost-Effective Current-Limiting Hybrid DC Circuit Breaker Based on Hybrid Semiconductors
by
Siyuan Liu, Ziao Yuan, Jinchao Chen, Yifan Chen, Mengze Yu, Zhiyuan Liu and Yingsan Geng
Electronics 2024, 13(10), 1948; https://doi.org/10.3390/electronics13101948 - 16 May 2024
Abstract
DC circuit breakers (DCCBs) are the key equipment to rapidly interrupt the fault current in high-voltage DC power grids and ensure the safe operation of the system. However, most DCCBs do not take current-limiting measures and rely solely on current-limiting reactors in the
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DC circuit breakers (DCCBs) are the key equipment to rapidly interrupt the fault current in high-voltage DC power grids and ensure the safe operation of the system. However, most DCCBs do not take current-limiting measures and rely solely on current-limiting reactors in the system to limit the rate of current rise during the interruption process. The extensive use of fully controlled power electronic devices in circuit breakers (CBs) results in high costs. To address the issues above, this paper proposes a DCCB topology with a current-limiting function based on thyristors and diodes, which can reduce the cost of CB while ensuring reliable interruption. The impact of various parameters on CB performance is analyzed using numerical calculations to optimize the parameters. Then, a simulation model of a 500 kV/16 kA DCCB is built in PSCAD/EMTDC, and the performance of the proposed CB topology is compared with the other CB topologies. By comparison, the proposed DCCB topology can reliably isolate fault currents and reduce the amplitude of fault currents and the cost of CBs. Significantly, the energy absorbed by the metal oxide varistor (MOV) during the interruption process decreases by 64.2%, reducing the cost and volume of the MOV. Finally, the feasibility of the CB is further verified in the ±500 kV 4-terminal high-voltage DC power grid simulation model. The results show that the proposed DCCB topology can limit the fault current rise rate, interrupt and isolate the fault reliably, and reduce the cost.
Full article
(This article belongs to the Section Industrial Electronics)
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Open AccessArticle
Correlation between CO2 Sensitivity and Channel-Layer Thickness in In2O3 Thin-Film Transistor Gas Sensors
by
Ayumu Nodera, Ryota Kobayashi, Tsubasa Kobayashi and Shinya Aikawa
Electronics 2024, 13(10), 1947; https://doi.org/10.3390/electronics13101947 - 16 May 2024
Abstract
CO2 monitoring is important for achieving net-zero emissions. Here, we report on a CO2 gas sensor based on an In2O3 thin-film transistor (TFT), which is expected to realize both low-temperature operation and high sensitivity. The effect of channel
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CO2 monitoring is important for achieving net-zero emissions. Here, we report on a CO2 gas sensor based on an In2O3 thin-film transistor (TFT), which is expected to realize both low-temperature operation and high sensitivity. The effect of channel thickness on TFT performance is well known; however, its effect on CO2 sensitivity has not been fully investigated. We fabricated In2O3 TFTs of various thicknesses to evaluate the effect of channel thickness on CO2 sensitivity. Consequently, TFT gas sensors with thinner channels exhibited higher CO2 sensitivity. This is because the surface effect is more prominent for a thinner film, suggesting that charge transfer between gas molecules and the channel surface through gas adsorption has a significant impact on changes in the TFT parameters in the subthreshold region. The results showed that the In2O3 TFT in thin channels is a promising candidate for CO2-sensitive TFT gas sensors and is useful for understanding an effect of gas adsorption in oxide TFTs with a very thin channel as well.
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(This article belongs to the Special Issue Feature Papers in Semiconductor Devices)
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Open AccessArticle
Development of an Uneven Terrain Decision-Aid Landing System for Fixed-Wing Aircraft Based on Computer Vision
by
Chin-Sheng Chuang and Chao-Chung Peng
Electronics 2024, 13(10), 1946; https://doi.org/10.3390/electronics13101946 - 15 May 2024
Abstract
This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly
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This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly and location-specific ground equipment, limiting their utility for low-payload light aircraft. Especially in emergency conditions, the pilot may be forced to land on an arbitrary runway where the road flatness and glide angle cannot be ensured. To address these issues, a stereo vision-based auxiliary landing system is proposed, which is capable of estimating an appropriate glide slope based on the terrain, to assist pilots in safe landing decision-making. Moreover, in real-world scenarios, challenges with visual-based methods arise when attempting emergency landings on complex terrains with diverse objects, such as roads and buildings. This study solves this problem by employing the Gaussian Mixture Model (GMM) to segment the color image and extract ground points, while the iterative weighted plane fitting (IWPF) algorithm is introduced to mitigate the interference of outlier feature points, reaching a highly robust plane normal estimation. With the aid of the proposed system, the pilot is able to evaluate the landing glide angle/speed with respect to the uneven terrain. Simulation results demonstrate that the proposed system can successfully achieve landing guidance in unknown environments by providing glide angle estimations with an average error of less than 1 degree.
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(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
Open AccessArticle
Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria
by
Chen Wu, Zhinong Wei, Xiangchen Jiang, Yizhen Huang and Donglou Fan
Electronics 2024, 13(10), 1945; https://doi.org/10.3390/electronics13101945 - 15 May 2024
Abstract
Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the
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Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million.
Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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