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
Feature Sparse Choosing VIT Model for Efficient Concrete Crack Segmentation in Portable Crack Measuring Devices
Electronics 2024, 13(9), 1641; https://doi.org/10.3390/electronics13091641 (registering DOI) - 25 Apr 2024
Abstract
►
Show Figures
Concrete crack measurement is important for concrete buildings. Deep learning-based segmentation methods have achieved state-of-art results. However, the model size of these models is extremely large which is impossible to use in portable crack measuring devices. To address this problem, a light-weight concrete
[...] Read more.
Concrete crack measurement is important for concrete buildings. Deep learning-based segmentation methods have achieved state-of-art results. However, the model size of these models is extremely large which is impossible to use in portable crack measuring devices. To address this problem, a light-weight concrete crack segmentation model based on the Feature Sparse Choosing VIT (LTNet) is proposed by us. In our proposed model, a Feature Sparse Choosing VIT (FSVIT) is used to reduce computational complexity in VIT as well as reducing the number of channels for crack features. In addition, a Feature Channel Selecting Module (FCSM) is proposed by us to reduce channel features as well as suppressing the influence of interfering features. Finally, Depthwise Separable Convolutions are used to substitute traditional convolutions for further reducing computational complexity. As a result, the model size of our LTNet is extremely small. Experimental results show that our LTNet could achieve an accuracy of 0.887, 0.817 and 0.693, and achieve a recall of 0.882, 0.805 and 0.681 on three datasets, respectively, which is 3–8% higher than current mainstream algorithms. However, the model size of our LTNet is only 2 M.
Full article
Open AccessArticle
Frequency Diversity Arc Array with Angle-Distance Two-Dimensional Broadening Null Steering for Sidelobe Suppression
by
Wei Xu, Ying Tian, Pingping Huang, Weixian Tan and Yaolong Qi
Electronics 2024, 13(9), 1640; https://doi.org/10.3390/electronics13091640 (registering DOI) - 24 Apr 2024
Abstract
The frequency diversity arc array (FDAA) improves the structure of the traditional frequency diversity array (FDA) from a linear array structure to an arc array structure, so that the FDAA not only has the advantages of the FDA but also has a large
[...] Read more.
The frequency diversity arc array (FDAA) improves the structure of the traditional frequency diversity array (FDA) from a linear array structure to an arc array structure, so that the FDAA not only has the advantages of the FDA but also has a large angle and omnidirectional scanning capability. However, when it is equivalent to a linear array, this arc-shaped structure will lead to the phenomenon of inverse density weighting, which leads to a higher sidelobe level of the FDAA beam pattern. In order to solve the problem of a high sidelobe level at a certain position of the FDAA, a frequency diversity arc array with angle-distance two-dimensional broadening null steering is proposed for sidelobe suppression. Using a structural model of the FDAA, the problem of the high sidelobe was analyzed. The linear constrained minimum variance (LCMV) method was used to generate a null with a certain width at the position of the fixed strong sidelobe level in the angle domain and the distance domain of the FDAA beam pattern, to reduce the FDAA sidelobe level. Then, the angle domain and distance domain fixed positions of the FDAA were simulated to generate the null beam pattern. The simulation results verified the effectiveness of this method for reducing the sidelobe level.
Full article
(This article belongs to the Special Issue Antenna Design and Its Applications)
Open AccessArticle
A Small Power Margin and Bandwidth Expansion Allow Data Transmission during Rainfall despite Large Attenuation: Application to GeoSurf Satellite Constellations at mm—Waves
by
Emilio Matricciani
Electronics 2024, 13(9), 1639; https://doi.org/10.3390/electronics13091639 (registering DOI) - 24 Apr 2024
Abstract
The traditional approach of considering the probability distribution of rain attenuation leads to provide very large power margin (overdesign) in data channels. We have extended a method which, with a small power margin, bandwidth expansion and variable symbol rate, avoids overdesign and can
[...] Read more.
The traditional approach of considering the probability distribution of rain attenuation leads to provide very large power margin (overdesign) in data channels. We have extended a method which, with a small power margin, bandwidth expansion and variable symbol rate, avoids overdesign and can transfer the same data volume as if the link were in clear—sky conditions. It is characterized only by the link mean efficiency, suitably defined. It is useful only if: (a) data must be up— and downloaded when it is raining; (b) real—time communication is not required. We have applied it to the links of GeoSurf satellite constellations (in which, at any latitude of ground stations, propagation paths are at the local zenith) by simulating rain attenuation time series at 80 GHz (mm—wave)—the new frontier of satellite frequencies—with the Synthetic Storm Technique, from rain—rate time series recorded on—site, at sites located in different climatic regions. The power margin to be implemented at 80 GHz ranges from 2.0 dB to 7.4 dB—well within the current technology—regardless the instantaneous rain attenuation. The clear—sky bandwidth is expanded 1.75 to 2.80 times, a factor not large per se, but it may challenge current technology if the clear—sky bandwidth is already large.
Full article
(This article belongs to the Special Issue Future Generation Non-Terrestrial Networks)
Open AccessArticle
Applications of the TL-Based Fault Diagnostic System for the Capacitor in Hybrid Aircraft
by
Maciej Skowron, Stanisław Oliszewski, Mateusz Dybkowski, Jeremi Jan Jarosz, Marcin Pawlak, Sebastien Weisse, Jerome Valire, Agnieszka Wyłomańska, Radosław Zimroz and Krzysztof Szabat
Electronics 2024, 13(9), 1638; https://doi.org/10.3390/electronics13091638 (registering DOI) - 24 Apr 2024
Abstract
The article concerns the problem of capacitor diagnosis in a hybrid aircraft. Capacitors are one of the most commonly damaged components of electrical vehicle drive systems. The result of these failures is an increase in voltage ripple. Most known analytical methods are based
[...] Read more.
The article concerns the problem of capacitor diagnosis in a hybrid aircraft. Capacitors are one of the most commonly damaged components of electrical vehicle drive systems. The result of these failures is an increase in voltage ripple. Most known analytical methods are based on frequency spectrum analysis, which is time-consuming and computationally complex. The use of deep neural networks (DNNs) allows for the direct use of the measurement signal, which reduces the operating time of the overall diagnostic system. However, the problem with these networks is the long training process. Therefore, this article uses transfer learning (TL), which allows for the secondary use of previously learnt DNNs. To collect data to learn the network, a test bench with the ability to simulate a capacitor failure was constructed, and a model based on it was made in the MATLAB/Simulink environment. A convolutional neural network (CNN) structure was developed and trained by the TL method to estimate the capacitance of the capacitor based on signals from the Simulink-designed model. The proposed fault diagnostic method is characterised by a nearly 100% efficiency in determining capacitance, with an operating time of about 10 ms, regardless of load and supply voltage.
Full article
(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)
Open AccessArticle
Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm
by
Chuanming Fu, Junfeng Liu, Jun Zeng and Ming Ma
Electronics 2024, 13(9), 1637; https://doi.org/10.3390/electronics13091637 (registering DOI) - 24 Apr 2024
Abstract
To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the
[...] Read more.
To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.
Full article
Open AccessArticle
Set-Up for Measuring Thermal Parameters of Power Semiconductor Devices
by
Krzysztof Górecki, Przemysław Ptak, Paweł Górecki and Aleksander Data
Electronics 2024, 13(9), 1636; https://doi.org/10.3390/electronics13091636 (registering DOI) - 24 Apr 2024
Abstract
In order to determine the junction temperature of semiconductor devices operating at different power supply and cooling conditions, their thermal parameters are needed. This article describes an original measurement set-up enabling the determination of thermal parameters of power semiconductor devices. In contrast to
[...] Read more.
In order to determine the junction temperature of semiconductor devices operating at different power supply and cooling conditions, their thermal parameters are needed. This article describes an original measurement set-up enabling the determination of thermal parameters of power semiconductor devices. In contrast to other set-ups described in the literature, this set-up makes it possible to measure thermal parameters which characterize the efficiency of the removal generated due to a self-heating phenomenon, as well as the parameters characterizing mutual thermal couplings. The presented set-up makes use of an indirect electrical method to determine the junction temperature of diodes, bipolar and unipolar transistors and IGBTs. The methods used to measure the self and transfer transient thermal impedances of these devices and the construction of the set-up are described. The influence of selected factors on the accuracy of the measurements is analyzed. Examples of the measurement results of thermal parameters (self and transfer transient thermal impedances) of power semiconductor devices operating at different cooling conditions are presented. The obtained research results are discussed.
Full article
(This article belongs to the Special Issue Advances in Modeling, Control and Protection of Power System Containing a High Proportion of Power Electronics)
Open AccessArticle
Enhancing Regular Expression Processing through Field-Programmable Gate Array-Based Multi-Character Non-Deterministic Finite Automata
by
Chuang Zhang, Xuebin Tang and Yuanxi Peng
Electronics 2024, 13(9), 1635; https://doi.org/10.3390/electronics13091635 (registering DOI) - 24 Apr 2024
Abstract
This work investigates the advantages of FPGA-based Multi-Character Non-Deterministic Finite Automata (MC-NFA) for enhancing regular expression processing over traditional software-based methods. By integrating Field-Programmable Gate Arrays (FPGAs) within a data processing framework, our study showcases significant improvements in processing efficiency, accuracy, and resource
[...] Read more.
This work investigates the advantages of FPGA-based Multi-Character Non-Deterministic Finite Automata (MC-NFA) for enhancing regular expression processing over traditional software-based methods. By integrating Field-Programmable Gate Arrays (FPGAs) within a data processing framework, our study showcases significant improvements in processing efficiency, accuracy, and resource utilization for complex pattern matching tasks. We present a novel approach that not only accelerates database and network security applications, but also contributes to the evolving landscape of computational efficiency and hardware acceleration. The findings illustrate that FPGA’s coherent access to main memory and the efficient use of resources lead to considerable gains in processing times and throughput for handling regular expressions, unaffected by expression complexity and driven primarily by dataset size and match location. Our research further introduces a phase shift compensation technique that elevates match accuracy to optimal levels, highlighting FPGA’s potential for real-time, accurate data processing. The study confirms that the benefits of using FPGA for these tasks do not linearly correlate with an increase in resource consumption, underscoring the technology’s efficiency. This paper not only solidifies the case for adopting FPGA technology in complex data processing tasks, but also lays the groundwork for future explorations into optimizing hardware accelerators for broader applications.
Full article
(This article belongs to the Topic Theory and Applications of High Performance Computing)
►▼
Show Figures
Figure 1
Open AccessArticle
Improvement of Practical Byzantine Fault Tolerance Consensus Algorithm Based on DIANA in Intellectual Property Environment Transactions
by
Jing Wang, Wenlong Feng, Mengxing Huang, Siling Feng and Dan Du
Electronics 2024, 13(9), 1634; https://doi.org/10.3390/electronics13091634 - 24 Apr 2024
Abstract
In response to the shortcomings of the consensus algorithm for intellectual property transactions, such as high communication overhead, random primary node selection, and prolonged consensus time, a Practical Byzantine Fault Tolerance (PBFT) improvement algorithm based on Divisive Analysis (DIANA) D-PBFT algorithm is proposed.
[...] Read more.
In response to the shortcomings of the consensus algorithm for intellectual property transactions, such as high communication overhead, random primary node selection, and prolonged consensus time, a Practical Byzantine Fault Tolerance (PBFT) improvement algorithm based on Divisive Analysis (DIANA) D-PBFT algorithm is proposed. Firstly, the algorithm adopts the hierarchical clustering mechanism of DIANA to cluster nodes based on similarity, enhancing node partition accuracy and reducing the number of participating consensus nodes. Secondly, it designs a reward and punishment system based on node ranking, to achieve consistency between node status and permissions, timely evaluation, and feedback on node behaviours, thereby enhancing node enthusiasm. Then, the election method of the primary node is improved by constructing proxy and alternate nodes and adopting a majority voting strategy to achieve the selection and reliability of the primary node. Finally, the consistency protocol is optimised to perform consensus once within the cluster and once between all primary nodes, to ensure the accuracy of the consensus results. Experimental results demonstrate that the D-PBFT algorithm shows a better performance, in terms of communication complexity, throughput, and latency.
Full article
Open AccessArticle
Tests of Fire Circuit Breakers (FCBs) to Assess Their Suitability for Use in Construction Objects
by
Tomasz Popielarczyk, Paweł Stępień, Michał Chmiel and Marta Iwańska
Electronics 2024, 13(9), 1633; https://doi.org/10.3390/electronics13091633 - 24 Apr 2024
Abstract
A fire circuit breaker (FCB) is dedicated to emergency services that can cut off the flow of electricity to all circuits, except for circuits supplying installations and equipment, the functioning of which is necessary during a fire. Theoretical research shows that there are
[...] Read more.
A fire circuit breaker (FCB) is dedicated to emergency services that can cut off the flow of electricity to all circuits, except for circuits supplying installations and equipment, the functioning of which is necessary during a fire. Theoretical research shows that there are no comprehensive studies on the FCB systems. Therefore, the aim of this study is to assess the impact of the components used (activating, signalling and executive devices) on the functionality of the entire FCB system (signal transmission time, actuation time, signalling, operational safety and resistance to various environmental conditions). This study proposes a new test scheme to evaluate the functionality of the entire FCB system, not just their individual components, which are widely known and used by electricians. However, it is only by combining them into a system, taking into account the requirements for firefighting equipment used by firefighters, that a completely new product is created. The new test scheme was properly validated by conducting a series of tests on several systems, consisting of multiple components (activating, signalling and executive devices). The tests carried out confirmed the validity of the assumptions made for the test methods and demonstrated the strong influence of the components (actuators, signalling and execution devices) on the functionality of the entire FCB system.
Full article
(This article belongs to the Special Issue Analog and Mixed Circuit: Design and Applications)
Open AccessArticle
Realization of Empathy Capability for the Evolution of Artificial Intelligence Using an MXene(Ti3C2)-Based Memristor
by
Yu Wang, Yanzhong Zhang, Yanji Wang, Hao Zhang, Xinpeng Wang, Rongqing Xu and Yi Tong
Electronics 2024, 13(9), 1632; https://doi.org/10.3390/electronics13091632 - 24 Apr 2024
Abstract
Empathy is the emotional capacity to feel and understand the emotions experienced by other human beings from within their frame of reference. As a unique psychological faculty, empathy is an important source of motivation to behave altruistically and cooperatively. Although human-like emotion should
[...] Read more.
Empathy is the emotional capacity to feel and understand the emotions experienced by other human beings from within their frame of reference. As a unique psychological faculty, empathy is an important source of motivation to behave altruistically and cooperatively. Although human-like emotion should be a critical component in the construction of artificial intelligence (AI), the discovery of emotional elements such as empathy is subject to complexity and uncertainty. In this work, we demonstrated an interesting electrical device (i.e., an MXene (Ti3C2) memristor) and successfully exploited the device to emulate a psychological model of “empathic blame”. To emulate this affective reaction, MXene was introduced into memristive devices because of its interesting structure and ionic capacity. Additionally, depending on several rehearsal repetitions, self-adaptive characteristic of the memristive weights corresponded to different levels of empathy. Moreover, an artificial neural system was designed to analogously realize a moral judgment with empathy. This work may indicate a breakthrough in making cool machines manifest real voltage-motivated feelings at the level of the hardware rather than the algorithm.
Full article
(This article belongs to the Special Issue New Insights into Memory/Storage Circuit, Architecture, and System)
►▼
Show Figures
Figure 1
Open AccessArticle
An Attitude Adaptive Integral Sliding Mode Control Algorithm with Disturbance Observer for Microsatellites to Track High-Speed Moving Targets
by
Xinyan Yang, Lei Li, Yurong Liao and Zhaoming Li
Electronics 2024, 13(9), 1631; https://doi.org/10.3390/electronics13091631 - 24 Apr 2024
Abstract
►▼
Show Figures
Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution, narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive
[...] Read more.
Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution, narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.03°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.
Full article
Figure 1
Open AccessArticle
Exploring Skin Interactions with 5G Millimeter-Wave through Fluorescence Lifetime Imaging Microscopy
by
Negin Foroughimehr, Andrew H. A. Clayton and Ali Yavari
Electronics 2024, 13(9), 1630; https://doi.org/10.3390/electronics13091630 - 24 Apr 2024
Abstract
The ongoing expansion of fifth-generation (5G) and future sixth-generation (6G) mobile communications is expected to result in widespread human exposure to millimeter-wave (mmWave) radiation globally. Given the short penetration depth of mmWaves and their high absorption by the skin, it is imperative to
[...] Read more.
The ongoing expansion of fifth-generation (5G) and future sixth-generation (6G) mobile communications is expected to result in widespread human exposure to millimeter-wave (mmWave) radiation globally. Given the short penetration depth of mmWaves and their high absorption by the skin, it is imperative to investigate the potential effects of 5G radiation not only in terms of temperature increase but also at the cellular level. To understand the biological mechanisms of mmWave effects, accurate methods for assessing mmWave absorption in the skin are crucial. In this study, we use fluorescence lifetime imaging microscopy (FLIM) to explore these effects. Employing a mmWave exposure system operating at 26 gigahertz (GHz), porcine skin is irradiated for varying durations (5, 10, 20, and 30 min). We investigate changes in tissue temperature and the autofluorescence of flavin adenine dinucleotide (FAD). Our findings suggest that operating our mmWave exposure systems at the configured power level of 26 GHz is unlikely to cause damage to FADs, even after a 30 min exposure duration.
Full article
(This article belongs to the Section Microwave and Wireless Communications)
►▼
Show Figures
Figure 1
Open AccessArticle
Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments
by
George Papageorgiou, Dimitrios Gkaimanis and Christos Tjortjis
Electronics 2024, 13(9), 1629; https://doi.org/10.3390/electronics13091629 - 24 Apr 2024
Abstract
Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo
[...] Read more.
Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.
Full article
(This article belongs to the Special Issue Machine Learning Advances and Applications on Natural Language Processing (NLP))
►▼
Show Figures
Figure 1
Open AccessFeature PaperArticle
AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)
by
Yibrah Gebreyesus, Damian Dalton, Davide De Chiara, Marta Chinnici and Andrea Chinnici
Electronics 2024, 13(9), 1628; https://doi.org/10.3390/electronics13091628 - 24 Apr 2024
Abstract
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP)
[...] Read more.
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.
Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
►▼
Show Figures
Graphical abstract
Open AccessCorrection
Correction: Srinivasan et al. Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images. Electronics 2023, 12, 862
by
Saravanan Srinivasan, Rajalakshmi Nagarnaidu Rajaperumal, Sandeep Kumar Mathivanan, Prabhu Jayagopal, Sujatha Krishnamoorthy and Seifedine Kardy
Electronics 2024, 13(9), 1627; https://doi.org/10.3390/electronics13091627 - 24 Apr 2024
Abstract
There was an error in the original publication [...]
Full article
Open AccessArticle
Interrupt Latency Accurate Measurement in Multiprocessing Embedded Systems by Means of a Dedicated Circuit
by
Sara Alonso, Leire Muguira, José Ignacio Garate, Carlos Cuadrado and Unai Bidarte
Electronics 2024, 13(9), 1626; https://doi.org/10.3390/electronics13091626 - 24 Apr 2024
Abstract
Modern multiprocessing embedded applications require, in many cases, two different environments on the same platform: one that meets real-time requirements and another one with a general purpose operating system. Although several technologies can be used, two of the most popular are virtualization based
[...] Read more.
Modern multiprocessing embedded applications require, in many cases, two different environments on the same platform: one that meets real-time requirements and another one with a general purpose operating system. Although several technologies can be used, two of the most popular are virtualization based on hypervisors and asymmetric multiprocessing software. However, using these tools introduces latency, which must be measured to verify compliance with real-time requirements. With the aim of facilitating these measurements, this work provides a hardware tool that is more precise and easier to use than other existing software solutions. The paper also studies the interrupt latency generated by different hypervisors and asymmetric multiprocessing frameworks in a Zynq UltraScale+ platform. This research work facilitates the accurate study of the temporal response of multiprocessor embedded systems, which allows for evaluating their suitability for applications with real-time requirements.
Full article
(This article belongs to the Special Issue Design and Development of Digital Embedded Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
Cooperative Lane-Change Control Method for Freeways Considering Dynamic Intelligent Connected Dedicated Lanes
by
Jian Xiang, Zhengwu Wang, Qi Mi, Qiang Wen and Zhuye Xu
Electronics 2024, 13(9), 1625; https://doi.org/10.3390/electronics13091625 - 24 Apr 2024
Abstract
Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a
[...] Read more.
Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a waste of road resources. This paper proposes a cooperative lane-changing control method for multiple vehicles considering Dynamic Intelligent Connected (DIC) dedicated lanes. Initially, inspired by the study of dedicated bus lanes, the paper elucidates the traffic regulations for DIC dedicated lanes, and two decision-making approaches are presented based on the type of lane-change vehicle and the target lane: CAV autonomous cooperative lane change and HDV mandatory cooperative lane change. Subsequently, considering constraints such as acceleration, speed, and safe headway, cooperative lane-change control models are proposed with the goal of minimizing the weighted sum of vehicle acceleration and lane-change duration. The proposed model is solved by the TOPSIS multi-objective optimization algorithm. Finally, the effectiveness and advancement of the proposed cooperative lane-changing method are validated through simulation using the SUMO software (Version 1.19.0). Simulation results demonstrate that compared to traditional lane-changing models, the autonomous cooperative lane-changing model for CAVs significantly improves the success rate of lane changing, reduces lane-changing time, and causes less speed disturbance to surrounding vehicles. The mandatory cooperative lane-changing model for HDVs results in shorter travel times and higher lane-changing success rates, especially under high traffic demand. The methods presented in this paper can notably enhance the lane-changing success rate and traffic efficiency while ensuring lane-changing safety.
Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
►▼
Show Figures
Figure 1
Open AccessArticle
CBin-NN: An Inference Engine for Binarized Neural Networks
by
Fouad Sakr, Riccardo Berta, Joseph Doyle, Alessio Capello, Ali Dabbous, Luca Lazzaroni and Francesco Bellotti
Electronics 2024, 13(9), 1624; https://doi.org/10.3390/electronics13091624 - 24 Apr 2024
Abstract
Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks
[...] Read more.
Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks (BNNs), we propose CBin-NN, a library of layer operators that allows the building of simple yet flexible convolutional neural networks (CNNs) with binary weights and activations. CBin-NN is platform-independent and is thus portable to virtually any software-programmable device. Experimental analysis on the CIFAR-10 dataset shows that our library, compared to a set of state-of-the-art inference engines, speeds up inference by 3.6 times and reduces the memory required to store model weights and activations by 7.5 times and 28 times, respectively, at the cost of slightly lower accuracy (2.5%). An ablation study stresses the importance of a Quantized Input Quantized Kernel Convolution layer to improve accuracy and reduce latency at the cost of a slight increase in model size.
Full article
(This article belongs to the Special Issue Edge Computing and Tiny Machine Learning in the Internet of Things: Latest Advances and Applications)
►▼
Show Figures
Figure 1
Open AccessArticle
Developing a Prototype Device for Assessing Meat Quality Using Autofluorescence Imaging and Machine Learning Techniques
by
Eric Zhou, Saabah B. Mahbub, Ewa M. Goldys and Sandhya Clement
Electronics 2024, 13(9), 1623; https://doi.org/10.3390/electronics13091623 - 24 Apr 2024
Abstract
Meat quality determination is now more vital than ever, with an ever-increasing demand for meat, especially with a greater desire for high-quality beef. Many existing qualitative methods currently used for meat quality assessment are strenuous, time-consuming, and subjective. The quantitative techniques employed are
[...] Read more.
Meat quality determination is now more vital than ever, with an ever-increasing demand for meat, especially with a greater desire for high-quality beef. Many existing qualitative methods currently used for meat quality assessment are strenuous, time-consuming, and subjective. The quantitative techniques employed are time-consuming, destructive, and expensive. In the search for a quantitative, rapid, and non-destructive method of determining meat quality, the use of autofluorescence has been employed and has demonstrated its capabilities to characterise meat grades by identifying biochemical features such as the intramuscular fat and tryptophan content through the excitation of meat samples and the collection and analysis of the emission data. Despite its success, the method remains expensive and inaccessible, thus preventing it from being translated into small-scale industry applications. This study will detail the process taken to design and construct a low-cost, miniature prototype device that could successfully distinguish between varying meat grades using autofluorescence imaging and machine learning techniques.
Full article
(This article belongs to the Special Issue New Advances in Optical Imaging and Metrology)
►▼
Show Figures
Figure 1
Open AccessArticle
An Algorithm for Distracted Driving Recognition Based on Pose Features and an Improved KNN
by
Yingjie Gong and Xizhong Shen
Electronics 2024, 13(9), 1622; https://doi.org/10.3390/electronics13091622 - 24 Apr 2024
Abstract
►▼
Show Figures
To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict
[...] Read more.
To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict distracted driving behavior. Firstly, the number of channels in the Lightweight OpenPose network is pruned to predict and output the coordinates of key points in the upper body of the driver. Secondly, based on the principles of ergonomics, driving behavior features are modeled, and a set of five-dimensional feature values are obtained through geometric calculations. Finally, considering the relationship between the distance between samples and the number of samples, this paper proposes an adjustable distance-weighted KNN algorithm (ADW-KNN), which is used for classification and prediction. The experimental results show that the proposed algorithm achieved a recognition rate of 94.04% for distracted driving behavior on the public dataset SFD3, with a speed of up to 50FPS, superior to mainstream deep learning algorithms in terms of accuracy and speed. The superiority of ADW-KNN was further verified through experiments on other public datasets.
Full article
Figure 1
Journal Menu
► ▼ Journal Menu-
- Electronics Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Applied Sciences, Designs, Electronics, Energies, JLPEA
Power Electronics Converters
Topic Editors: Mohsin Jamil, Yuanmao Ye, Tomasz PajchrowskiDeadline: 30 April 2024
Topic in
Coatings, Electronics, JSAN, Nanomaterials, Sensors
Modeling, Fabrication, and Characterization of Semiconductor Materials and Devices
Topic Editors: Andrei Avram, Ana-Maria Lepadatu, Florin Nastase, Martino AldrigoDeadline: 15 May 2024
Topic in
Energies, Materials, Electronics, Machines, WEVJ
Advanced Electrical Machine Design and Optimization Ⅱ
Topic Editors: Youguang Guo, Gang Lei, Xin BaDeadline: 31 May 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Conferences
Special Issues
Special Issue in
Electronics
Machine Learning for Biomedical Applications
Guest Editor: William M. MonganDeadline: 26 April 2024
Special Issue in
Electronics
Advance of Cooperative Working in Design, Visualization and Engineering
Guest Editors: Yuhua Luo, Tony HuangDeadline: 5 May 2024
Special Issue in
Electronics
Application of Power Electronics Technology in Energy System
Guest Editors: Jizhong Zhu, Lei Xi, Yun Liu, Weiye ZhengDeadline: 15 May 2024
Special Issue in
Electronics
Network Intrusion Detection Using Deep Learning
Guest Editor: Harald VrankenDeadline: 31 May 2024
Topical Collections
Topical Collection in
Electronics
Application of Advanced Computing, Control and Processing in Engineering
Collection Editors: Sudip Chakraborty, Robertas Damaševičius, Sergio Greco
Topical Collection in
Electronics
Instrumentation, Noise, Reliability
Collection Editor: Graziella Scandurra
Topical Collection in
Electronics
Computer Vision and Pattern Recognition Techniques
Collection Editor: Donghyeon Cho
Topical Collection in
Electronics
Deep Learning for Computer Vision: Algorithms, Theory and Application
Collection Editors: Jungong Han, Guiguang Ding