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
Engineering, Emulators, Digital Twins, and Performance Engineering
Electronics 2024, 13(10), 1829; https://doi.org/10.3390/electronics13101829 - 8 May 2024
Abstract
Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance
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Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance engineering, aiming at adaptable monitoring diagnostic, prognostic, and prescriptive capabilities. A key element introduced here is the role of emulators in this transformation. Emulators, also called surrogate models or metamodels, provide monitoring and diagnostic capabilities. In particular, we focus on an optimisation goal combining optimised and robust performance derived from stochastic emulators. We demonstrate the methodology using two open-source examples and show how emulators can be used to complement finite element and computational fluid dynamic models in digital twin frameworks. The case studies consist of a mechanical system and a biological production process.
Full article
(This article belongs to the Special Issue Digital Twins in Industry 4.0)
Open AccessArticle
Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case
by
Saray Rufo, Lidia Aguiar-Castillo, Julio Rufo and Rafael Perez-Jimenez
Electronics 2024, 13(10), 1828; https://doi.org/10.3390/electronics13101828 - 8 May 2024
Abstract
This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical
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This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.
Full article
(This article belongs to the Special Issue Next-Generation Indoor Wireless Communication)
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Open AccessArticle
Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks
by
Xuan-Toan Dang and Oh-Soon Shin
Electronics 2024, 13(10), 1827; https://doi.org/10.3390/electronics13101827 - 8 May 2024
Abstract
Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones,
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Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.
Full article
(This article belongs to the Special Issue Sixth-Generation Wireless Communications: Theory and Applications)
Open AccessArticle
Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface
by
Qiangqiang Yang, Yufeng Chen, Zhiyu Huang, Hongwen Yu and Yong Fang
Electronics 2024, 13(10), 1826; https://doi.org/10.3390/electronics13101826 - 8 May 2024
Abstract
This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to
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This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.
Full article
(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)
Open AccessArticle
Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search
by
Oleksandr Kuznetsov, Nikolay Poluyanenko, Emanuele Frontoni, Sergey Kandiy, Mikolaj Karpinski and Ruslan Shevchuk
Electronics 2024, 13(10), 1825; https://doi.org/10.3390/electronics13101825 - 8 May 2024
Abstract
The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences
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The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.
Full article
(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)
Open AccessArticle
Current-Prediction-Controlled Quasi-Z-Source Cascaded Multilevel Photovoltaic Inverter
by
Shanshan Lei, Ningzhi Jin and Jiaxin Jiang
Electronics 2024, 13(10), 1824; https://doi.org/10.3390/electronics13101824 - 8 May 2024
Abstract
To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a
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To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory.
Full article
(This article belongs to the Special Issue Power Electronics in Renewable Systems)
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Open AccessArticle
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification
by
Ruihua Zhou, Qi Wang, Lei Cao, Jianqiang Xu, Xiaogang Zhu, Xin Xiong, Huiqi Zhang and Yuling Zhong
Electronics 2024, 13(10), 1823; https://doi.org/10.3390/electronics13101823 - 8 May 2024
Abstract
The definition of vehicle viewpoint annotations is ambiguous due to human subjective judgment, which makes the cross-domain vehicle re-identification methods unable to learn the viewpoint invariance features during source domain pre-training. This will further lead to cross-view misalignment in downstream target domain tasks.
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The definition of vehicle viewpoint annotations is ambiguous due to human subjective judgment, which makes the cross-domain vehicle re-identification methods unable to learn the viewpoint invariance features during source domain pre-training. This will further lead to cross-view misalignment in downstream target domain tasks. To solve the above challenges, this paper presents a dual-level viewpoint-learning framework that contains an angle invariance pre-training method and a meta-orientation adaptation learning strategy. The dual-level viewpoint-annotation proposal is first designed to concretely redefine the vehicle viewpoint from two aspects (i.e., angle-level and orientation-level). An angle invariance pre-training method is then proposed to preserve identity similarity and difference across the cross-view; this consists of a part-level pyramidal network and an angle bias metric loss. Under the supervision of angle bias metric loss, the part-level pyramidal network, as the backbone, learns the subtle differences of vehicles from different angle-level viewpoints. Finally, a meta-orientation adaptation learning strategy is designed to extend the generalization ability of the re-identification model to the unseen orientation-level viewpoints. Simultaneously, the proposed meta-learning strategy enforces meta-orientation training and meta-orientation testing according to the orientation-level viewpoints in the target domain. Extensive experiments on public vehicle re-identification datasets demonstrate that the proposed method combines the redefined dual-level viewpoint-information and significantly outperforms other state-of-the-art methods in alleviating viewpoint variations.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Computer Vision)
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Investigation of Single-Event Effects for Space Applications: Instrumentation for In-Depth System Monitoring
by
André M. P. Mattos, Douglas A. Santos, Lucas M. Luza, Viyas Gupta and Luigi Dilillo
Electronics 2024, 13(10), 1822; https://doi.org/10.3390/electronics13101822 - 8 May 2024
Abstract
Ionizing radiation induces the degradation of electronic systems. For memory devices, this phenomenon is often observed as the corruption of the stored data and, in some cases, the occurrence of sudden increases in current consumption during the operation. In this work, we propose
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Ionizing radiation induces the degradation of electronic systems. For memory devices, this phenomenon is often observed as the corruption of the stored data and, in some cases, the occurrence of sudden increases in current consumption during the operation. In this work, we propose enhanced experimental instrumentation to perform in-depth Single-Event Effects (SEE) monitoring and analysis of electronic systems. In particular, we focus on the Single-Event Latch-up (SEL) phenomena in memory devices, in which current monitoring and control are required for testing. To expose the features and function of the proposed instrumentation, we present results for a case study of an SRAM memory that has been used on-board PROBA-V ESA satellite. For this study, we performed experimental campaigns in two different irradiation facilities with protons and heavy ions, demonstrating the instrumentation capabilities, such as synchronization, high sampling rate, fast response time, and flexibility. Using this instrumentation, we could report the cross section for the observed SEEs and further investigate their correlation with the observed current behavior. Notably, it allowed us to identify that 95% of Single-Event Functional Interrupts (SEFIs) were triggered during SEL events.
Full article
(This article belongs to the Special Issue New Insights in Radiation-Tolerant Electronics)
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GLRM: Geometric Layout-Based Resource Management Method on Multiple Field Programmable Gate Array Systems
by
Hongxu Gao, Zeyu Li, Lirong Zhou, Xiang Li and Quan Wang
Electronics 2024, 13(10), 1821; https://doi.org/10.3390/electronics13101821 - 8 May 2024
Abstract
Multiple field programmable gate array (Multi-FPGA) systems are capable of forming larger and more powerful computing units through high-speed interconnections between chips and are beginning to be widely used by various computing service providers. However, the new computing architecture brings new challenges to
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Multiple field programmable gate array (Multi-FPGA) systems are capable of forming larger and more powerful computing units through high-speed interconnections between chips and are beginning to be widely used by various computing service providers. However, the new computing architecture brings new challenges to the system’s task resource management. Existing resource management methods do not fully exploit resources in Multi-FPGA systems, and it is difficult to support fast resource request and release. In this regard, we propose a geometric layout-based resource management (GLRM) method for Multi-FPGA systems. First, a geometric layout-based task combination algorithm (TCA) was proposed to ensure that the final system can use the available FPGA resources more efficiently. Then, we optimised two resource management algorithms using TCA. Compared with state-of-the-art resource management methods, TCA increases resource flexibility by an average of 6% and resource utilisation by an average of 7%, and the two optimised resource management methods are effective in improving resource management performance.
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(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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Model Simplification for Asymmetric Marine Vehicles in Horizontal Motion—Verification of Selected Tracking Control Algorithms
by
Przemyslaw Herman
Electronics 2024, 13(10), 1820; https://doi.org/10.3390/electronics13101820 - 8 May 2024
Abstract
This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the
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This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the application condition was that the vehicle was symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches.
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(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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Design and Development of Multi-Agent Reinforcement Learning Intelligence on the Robotarium Platform for Embedded System Applications
by
Lorenzo Canese, Gian Carlo Cardarilli, Mohammad Mahdi Dehghan Pir, Luca Di Nunzio and Sergio Spanò
Electronics 2024, 13(10), 1819; https://doi.org/10.3390/electronics13101819 - 8 May 2024
Abstract
This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm for robotic applications. This study investigates the efficacy of Q-RTS in the reducing convergence time to a satisfactory movement policy through the successful implementation of four and
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This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm for robotic applications. This study investigates the efficacy of Q-RTS in the reducing convergence time to a satisfactory movement policy through the successful implementation of four and eight trained agents. Q-RTS has been shown to significantly reduce search time in terms of training iterations, from almost a million iterations with one agent to 650,000 iterations with four agents and 500,000 iterations with eight agents. The scalability of the algorithm was addressed by testing it on several agents’ configurations. A central focus was placed on the design of a sophisticated reward function, considering various postures of the agents and their critical role in optimizing the Q-learning algorithm. Additionally, this study delved into the robustness of trained agents, revealing their ability to adapt to dynamic environmental changes. The findings have broad implications for improving the efficiency and adaptability of robotic systems in various applications such as IoT and embedded systems. The algorithm was tested and implemented using the Georgia Tech Robotarium platform, showing its feasibility for the above-mentioned applications.
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(This article belongs to the Special Issue Applied Machine Learning in Intelligent Systems)
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Comparative Analysis of Thermal Properties in Molybdenum Substrate to Silicon and Glass for a System-on-Foil Integration
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Tzu-Jung Huang, Tobias Kiebala, Paul Suflita, Chad Moore, Graeme Housser, Shane McMahon and Ivan Puchades
Electronics 2024, 13(10), 1818; https://doi.org/10.3390/electronics13101818 - 8 May 2024
Abstract
Advanced electronics technology is moving towards smaller footprints and higher computational power. In order to achieve this, advanced packaging techniques are currently being considered, including organic, glass, and semiconductor-based substrates that allow for 2.5D or 3D integration of chips and devices. Metal-core substrates
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Advanced electronics technology is moving towards smaller footprints and higher computational power. In order to achieve this, advanced packaging techniques are currently being considered, including organic, glass, and semiconductor-based substrates that allow for 2.5D or 3D integration of chips and devices. Metal-core substrates are a new alternative with similar properties to those of semiconductor-based substrates but with the added benefits of higher flexibility and metal ductility. This work comprehensively compares the thermal properties of a novel metal-based substrate, molybdenum, and silicon and fused silica glass substrates in the context of system-on-foil (SoF) integration. A simple electronic technique is used to simulate the heat generated by a typical CPU and to measure the heat dissipation properties of the substrates. The results indicate that molybdenum and silicon are able to effectively dissipate a continuous power density of 2.3 W/mm2 as the surface temperature only increases by ~15 °C. In contrast, the surface temperature of fused silica glass substrates increases by >140 °C for the same applied power. These simple techniques and measurements were validated with infrared camera measurements as well as through finite element analysis via COMSOL simulation. The results validate the use of molybdenum as an advanced packaging substrate and can be used to characterize new substrates and approaches for advanced packaging.
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(This article belongs to the Special Issue Advances in Optical Communication and Optical Computing)
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Automotive Parts Defect Detection Based on YOLOv7
by
Hao Huang and Kai Zhu
Electronics 2024, 13(10), 1817; https://doi.org/10.3390/electronics13101817 - 8 May 2024
Abstract
Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection
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Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection algorithms have been proven to be ineffective, which often leads to missing detection or wrong identification. To address these issues, this paper introduces a defect detection algorithm based on YOLOv7. To enhance the detection of small objects and streamline the model, we incorporate the ECA attention mechanism into the network structure’s backbone. Considering the small sizes of defect targets on automotive parts and the complexity of their backgrounds, we redesign the neck portion of the model. This redesign includes the integration of the BiFPN feature fusion module to enhance feature fusion, with the aim of minimizing missed detections and false alarms. Additionally, we employ the Alpha-IoU loss function in the prediction phase to enhance the model’s accuracy, which is crucial for reducing false detection. The IoU loss function also boosts the model’s efficiency at converging. The evaluation of this model utilized the Northeastern University steel dataset and a proprietary dataset and demonstrated that the average accuracy (mAP) of the MBEA-YOLOv7 detection network was 76.2% and 94.1%, respectively. These figures represent improvements of 5.7% and 4.7% over the original YOLOv7 network. Moreover, the detection speed for individual images ranges between 1–2 ms. This enhancement in detection accuracy for small targets does not compromise detection speed, fulfilling the requirements for real-time, dynamic inspection of defects.
Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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CogCol: Code Graph-Based Contrastive Learning Model for Code Summarization
by
Yucen Shi, Ying Yin, Mingqian Yu and Liangyu Chu
Electronics 2024, 13(10), 1816; https://doi.org/10.3390/electronics13101816 - 8 May 2024
Abstract
Summarizing source code by natural language aims to help developers better understand existing code, making software development more efficient. Since source code is highly structured, recent research uses code structure information like Abstract Semantic Tree (AST) to enhance the structure understanding rather than
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Summarizing source code by natural language aims to help developers better understand existing code, making software development more efficient. Since source code is highly structured, recent research uses code structure information like Abstract Semantic Tree (AST) to enhance the structure understanding rather than a normal translation task. However, AST can only represent the syntactic relationship of code snippets, which can not reflect high-level relationships like control and data dependency in the program dependency graph. Moreover, researchers treat the AST as the unique structure information of one code snippet corresponding to one summarization. It will be easily affected by simple perturbations as it lacks the understanding of code with similar structure. To handle the above problems, we build CogCol, a Code graph-based Contrastive learning model. CogCol is a Transformer-based model that converts code graphs into unique sequences to enhance the model’s structure learning. In detail, CogCol uses supervised contrastive learning by building several kinds of code graphs as positive samples to enhance the structural representation of code snippets and generalizability. Moreover, experiments on the widely used open-source dataset show that CogCol can significantly improve the state-of-the-art code summarization models under Meteor, BLEU, and ROUGE.
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(This article belongs to the Special Issue Data Mining Applied in Natural Language Processing)
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One-Shot Federated Learning with Label Differential Privacy
by
Zikang Chen, Changli Zhou and Zhenyu Jiang
Electronics 2024, 13(10), 1815; https://doi.org/10.3390/electronics13101815 - 8 May 2024
Abstract
Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to
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Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to perform well on unbalanced datasets. Furthermore, the increased complexity of networks makes the application of traditional differential privacy to protect client privacy expensive. In this context, the authors introduce FedGM: a method designed to reduce communication overhead and achieve outstanding results in non-IID scenarios. FedGM is capable of achieving considerable accuracy, even with a small privacy budget. Specifically, the authors devise a method to extract knowledge from each client’s data by creating a scaled-down but highly effective synthesized dataset that can perform similarly to the original data. Additionally, the authors propose an innovative approach to applying label differential privacy to protect the synthesized dataset. The authors demonstrate the superiority of the approach over traditional methods by requiring only one communication round and by testing using four classification datasets for evaluation. Furthermore, when comparing the model performance for clients using their method against traditional solutions, the authors find that the approach achieves significant accuracy and better privacy.
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(This article belongs to the Special Issue AI for Edge Computing)
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A 5K Efficient Low-Light Enhancement Model by Estimating Increment between Dark Image and Transmission Map Based on Local Maximum Color Value Prior
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Qikang Deng, Dongwon Choo, Hyochul Ji and Dohoon Lee
Electronics 2024, 13(10), 1814; https://doi.org/10.3390/electronics13101814 - 8 May 2024
Abstract
Low-light enhancement (LLE) has seen significant advancements over decades, leading to substantial improvements in image quality that even surpass ground truth. However, these advancements have come with a downside as the models grew in size and complexity, losing their lightweight and real-time capabilities
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Low-light enhancement (LLE) has seen significant advancements over decades, leading to substantial improvements in image quality that even surpass ground truth. However, these advancements have come with a downside as the models grew in size and complexity, losing their lightweight and real-time capabilities crucial for applications like surveillance, autonomous driving, smartphones, and unmanned aerial vehicles (UAVs). To address this challenge, we propose an exceptionally lightweight model with just around 5K parameters, which is capable of delivering high-quality LLE results. Our method focuses on estimating the incremental changes from dark images to transmission maps based on the low maximum color value prior, and we introduce a novel three-channel transmission map to capture more details and information compared to the traditional one-channel transmission map. This innovative design allows for more effective matching of incremental estimation results, enabling distinct transmission adjustments to be applied to the R, G, and B channels of the image. This streamlined approach ensures that our model remains lightweight, making it suitable for deployment on low-performance devices without compromising real-time performance. Our experiments confirm the effectiveness of our model, achieving high-quality LLE comparable to the IAT (local) model. Impressively, our model achieves this level of performance while utilizing only 0.512 GFLOPs and 4.7K parameters, representing just 39.1% of the GFLOPs and 23.5% of the parameters used by the IAT (local) model.
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(This article belongs to the Special Issue Advanced Theories and Applications of Multimedia Information Technology (Invited Papers from MITA 2023))
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Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder
by
Xiuchun Lin, Yichao Liu, Chen Feng, Zhide Chen, Xu Yang and Hui Cui
Electronics 2024, 13(10), 1813; https://doi.org/10.3390/electronics13101813 - 7 May 2024
Abstract
Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and
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Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements.
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(This article belongs to the Special Issue Advances in Image Processing and Computer Vision Based on Machine Learning)
Open AccessArticle
Dependence of Body Stability on Optical Conditions during VR Viewing
by
Gi-Seong Jeong, Hyun-Goo Kang and Sang-Yeob Kim
Electronics 2024, 13(10), 1812; https://doi.org/10.3390/electronics13101812 - 7 May 2024
Abstract
The dependence of body stability on the distance between the optical centers of VR-device lenses and the refractive error status of users during VR viewing was investigated. Participants included 31 adults, and their postural-control ability was measured using a BTrackS device. The optical
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The dependence of body stability on the distance between the optical centers of VR-device lenses and the refractive error status of users during VR viewing was investigated. Participants included 31 adults, and their postural-control ability was measured using a BTrackS device. The optical conditions were (1) COCD (comfortable optical center distance), (2) COCD+2D (comfortable optical center distance with 2D myopia), (3) COCD-2D (comfortable optical center distance with 2D hyperopia), (4) DOCD (uncomfortable optical center distance), (5) DOCD+2D (uncomfortable optical center distance with 2D myopia), and (6) DOCD-2D (uncomfortable optical center distance with 2D hyperopia). Posture was assessed under these six optical conditions while the participants were wearing a VR device and watching a 3D roller-coaster video. The sway-path length was significantly increased under the COCD-2D, DOCD, DOCD+2D, and DOCD-2D conditions compared to the COCD condition (p < 0.05). In the case of maximum sway velocity, the results showed significant increases under the DOCD, DOCD+2D, and DOCD-2D conditions compared to the COCD condition (p < 0.05). The analysis revealed that when users are viewing VR displays, optimization of the distance to the optical center of the VR-device lenses and correction of the refractive errors for individual users was a significant factor in minimizing body instability.
Full article
(This article belongs to the Special Issue Applications of Virtual, Augmented and Mixed Reality)
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Open AccessArticle
Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems
by
Andrzej Radecki and Tomasz Rybicki
Electronics 2024, 13(10), 1811; https://doi.org/10.3390/electronics13101811 - 7 May 2024
Abstract
Virtual simulators of embedded systems and analyses of student surveys regarding their use at the early stage of the process of learning embedded systems, are presented in this article. The questionnaires were prepared in the Polish language, and the answers were automatically translated
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Virtual simulators of embedded systems and analyses of student surveys regarding their use at the early stage of the process of learning embedded systems, are presented in this article. The questionnaires were prepared in the Polish language, and the answers were automatically translated into English using two publicly available translators. The results of users’ experiences and feelings related to the use of virtual simulators are shown on the basis of detected sentiment using three chosen analysis methods: the Flair NLP library, the Pattern library, and the BERT NLP model. The results of the selected sentiment detection methods were compared and related to users reference answers, which gives information about the methods quality of the methods and their possible use in the automated review analysis process. This paper comprises detailed sentiment analysis results with a broader statistical approach for each question. Based on the students feedback and sentiment analysis, a new version of the TMSLAB v.2 virtual simulator was created.
Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
Open AccessArticle
Spacecraft Medium Voltage Direct-Current (MVDC) Power and Propulsion System
by
Sarah Talebzadeh and Omid Beik
Electronics 2024, 13(10), 1810; https://doi.org/10.3390/electronics13101810 - 7 May 2024
Abstract
This paper introduces a medium voltage direct-current (MVDC) system for large spacecraft megawatt-scale (MW) power and propulsion systems intended for interplanetary transport, including missions to the Moon and Mars. The proposed MVDC system includes: (i) A nuclear electric propulsion (NEP) that powers a
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This paper introduces a medium voltage direct-current (MVDC) system for large spacecraft megawatt-scale (MW) power and propulsion systems intended for interplanetary transport, including missions to the Moon and Mars. The proposed MVDC system includes: (i) A nuclear electric propulsion (NEP) that powers a permanent magnet (PM) generator whose output is rectified and connected to the MVDC bus. (ii) A solar photovoltaic (PV) source that is interfaced to the MVDC bus using a unidirectional boost DC-DC converter. (iii) A backup battery energy storage system (BESS) that connects to the MVDC bus using a bidirectional DC-DC boost converter. (iv) A dual active bridge (DAB) converter that controls the power to the spacecraft’s electric thruster. The NEP serves as the main power source for the spacecraft’s electric thruster, while the solar PV and BESS are intended to provide power for the payload and spacecraft’s low-voltage power system. The paper will (i) provide a review of the spacecraft MVDC power and prolusion system highlighting state-of-the-art main components, (ii) address the control of boost converters for the PV and BESS sources and the DAB converter for the thruster, and (iii) propose an uncertainty and disturbance estimator (UDE) concept based on current control algorithms to mitigate MVDC instability due to unpredictable factors and external disruptions. The proposed UDE can actively estimate and compensate for the system disturbance and uncertainty in real time, and thus, both the system tracking performance and robustness can be improved. Simulation studies have been conducted to substantiate the efficacy of the proposed schemes.
Full article
(This article belongs to the Section Power Electronics)
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