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
Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture
Electronics 2024, 13(11), 2057; https://doi.org/10.3390/electronics13112057 (registering DOI) - 24 May 2024
Abstract
The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness.
[...] Read more.
The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity.
Full article
(This article belongs to the Special Issue Recent Advances in Modelling, Control and Navigation of Ground and Aerial Robots)
Open AccessArticle
Deep Learning-Based Causal Inference Architecture and Algorithm between Stock Closing Price and Relevant Factors
by
Wanqi Xing, Chi Chen and Lei Xue
Electronics 2024, 13(11), 2056; https://doi.org/10.3390/electronics13112056 (registering DOI) - 24 May 2024
Abstract
Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning algorithms, thereby failing to fully exploit the formidable
[...] Read more.
Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning algorithms, thereby failing to fully exploit the formidable computational capabilities of deep learning models. Moreover, the inference of causal relationships largely depends on the Granger causality test, which is not suitable for non-stationary and non-linear stock factors. Also, most existing studies do not consider the impact of confounding variables or further validation of causal relationships. In response to the current research deficiencies, this paper introduces a deep learning-based algorithm aimed at inferring causal relationships between stock closing prices and relevant factors. To achieve this, causal diagrams from the structural causal model (SCM) were integrated into the analysis of stock data. Subsequently, a sliding window strategy combined with Gated Recurrent Units (GRUs) was employed to predict the potential values of closing prices, and a grouped architecture was constructed inspired by the Potential Outcomes Framework (POF) for controlling confounding variables. The architecture was employed to infer causal relationships between closing price and relevant factors through the non-linear Granger causality test. Finally, comparative experimental results demonstrate a marked enhancement in the accuracy and performance of closing price predictions when causal factors were incorporated into the prediction model. This finding not only validates the correctness of the causal inference, but also strengthens the reliability and validity of the proposed methodology. Consequently, this study has significant practical implications for the analysis of causality in financial time series data and the prediction of stock prices.
Full article
Open AccessReview
A Systematic Literature Review on Using Natural Language Processing in Software Requirements Engineering
by
Sabina-Cristiana Necula, Florin Dumitriu and Valerică Greavu-Șerban
Electronics 2024, 13(11), 2055; https://doi.org/10.3390/electronics13112055 - 24 May 2024
Abstract
This systematic literature review examines the integration of natural language processing (NLP) in software requirements engineering (SRE) from 1991 to 2023. Focusing on the enhancement of software requirement processes through technological innovation, this study spans an extensive array of scholarly articles, conference papers,
[...] Read more.
This systematic literature review examines the integration of natural language processing (NLP) in software requirements engineering (SRE) from 1991 to 2023. Focusing on the enhancement of software requirement processes through technological innovation, this study spans an extensive array of scholarly articles, conference papers, and key journal and conference reports, including data from Scopus, IEEE Xplore, ACM Digital Library, and Clarivate. Our methodology employs both quantitative bibliometric tools, like keyword trend analysis and thematic mapping, and qualitative content analysis to provide a robust synthesis of current trends and future directions. Reported findings underscore the essential roles of advanced computational techniques like machine learning, deep learning, and large language models in refining and automating SRE tasks. This review highlights the progressive adoption of these technologies in response to the increasing complexity of software systems, emphasizing their significant potential to enhance the accuracy and efficiency of requirement engineering practices while also pointing to the challenges of integrating artificial intelligence (AI) and NLP into existing SRE workflows. The systematic exploration of both historical contributions and emerging trends offers new insights into the dynamic interplay between technological advances and their practical applications in SRE.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI (XAI))
Open AccessArticle
Research on a Personalized Decision Control Algorithm for Autonomous Vehicles Based on the Reinforcement Learning from Human Feedback Strategy
by
Ning Li and Pengzhan Chen
Electronics 2024, 13(11), 2054; https://doi.org/10.3390/electronics13112054 - 24 May 2024
Abstract
To address the shortcomings of previous autonomous decision models, which often overlook the personalized features of users, this paper proposes a personalized decision control algorithm for autonomous vehicles based on RLHF (reinforcement learning from human feedback). The algorithm combines two reinforcement learning approaches,
[...] Read more.
To address the shortcomings of previous autonomous decision models, which often overlook the personalized features of users, this paper proposes a personalized decision control algorithm for autonomous vehicles based on RLHF (reinforcement learning from human feedback). The algorithm combines two reinforcement learning approaches, DDPG (Deep Deterministic Policy Gradient) and PPO (proximal policy optimization), and divides the control scheme into three phases including pre-training, human evaluation, and parameter optimization. During the pre-training phase, an agent is trained using the DDPG algorithm. In the human evaluation phase, different trajectories generated by the DDPG-trained agent are scored by individuals with different styles, and the respective reward models are trained based on the trajectories. In the parameter optimization phase, the network parameters are updated using the PPO algorithm and the reward values given by the reward model to achieve personalized autonomous vehicle control. To validate the control algorithm designed in this paper, a simulation scenario was built using CARLA_0.9.13 software. The results demonstrate that the proposed algorithm can provide personalized decision control solutions for different styles of people, satisfying human needs while ensuring safety.
Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
Open AccessArticle
Curved Domains in Magnetics: A Virtual Element Method Approach for the T.E.A.M. 25 Benchmark Problem
by
Franco Dassi, Paolo Di Barba and Alessandro Russo
Electronics 2024, 13(11), 2053; https://doi.org/10.3390/electronics13112053 - 24 May 2024
Abstract
In this paper, we are interested in solving optimal shape design problems. A critical challenge within this framework is generating the mesh of the computational domain at each optimisation step according to the information provided by the minimising functional. To enhance efficiency, we
[...] Read more.
In this paper, we are interested in solving optimal shape design problems. A critical challenge within this framework is generating the mesh of the computational domain at each optimisation step according to the information provided by the minimising functional. To enhance efficiency, we propose a strategy based on the Finite Element Method (FEM) and the Virtual Element Method (VEM). Specifically, we exploit the flexibility of the VEM in dealing with generally shaped polygons, including those with hanging nodes, to update the mesh solely in regions where the shape varies. In the remaining parts of the domain, we employ the FEM, known for its robustness and applicability in such scenarios. We numerically validate the proposed approach on the T.E.A.M. 25 benchmark problem and compare the results obtained with this procedure with those proposed in the literature based solely on the FEM. Moreover, since the T.E.A.M. 25 benchmark problem is also characterised by curved shapes, we utilise the VEM to accurately incorporate these “exact” curves into the discrete solution itself.
Full article
(This article belongs to the Section Microelectronics)
Open AccessArticle
Adaptive Mobility-Based IoT LoRa Clustering Communication Scheme
by
Dick Mugerwa, Youngju Nam, Hyunseok Choi, Yongje Shin and Euisin Lee
Electronics 2024, 13(11), 2052; https://doi.org/10.3390/electronics13112052 - 24 May 2024
Abstract
Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such
[...] Read more.
Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.
Full article
(This article belongs to the Special Issue Ubiquitous Sensor Networks II)
►▼
Show Figures
Figure 1
Open AccessArticle
Integration and Implementation of Scaled Agile Framework and V-Model in the Healthcare Sector Organization
by
Marcela Pavlíčková, Andrea Mojžišová, Zuzana Bodíková, Richard Szeplaki and Marek Laciak
Electronics 2024, 13(11), 2051; https://doi.org/10.3390/electronics13112051 - 24 May 2024
Abstract
The development of medical technology devices leads to the introduction and use of agile methods, which enable the delivery of increasingly complex software with the fastest possible innovations. Delivery of the highest quality software must be considered during development, as medical products are
[...] Read more.
The development of medical technology devices leads to the introduction and use of agile methods, which enable the delivery of increasingly complex software with the fastest possible innovations. Delivery of the highest quality software must be considered during development, as medical products are important elements in saving human lives. Their development begins with determining a set of product requirements that exactly correspond to it. The development of specified medical products is finally delivered to the customer, who participates in the development. In this article, we present the use and combination of agile methods in software development, which correct and facilitate timely and continuous delivery of products. They also know how to smooth out a quick reaction to the customer’s changing needs and mainly focus on team management and communication. Specific agile methods make it possible to implement development through gradual improvements by integrating customer requirements towards the product. This article identifies three interconnected approaches to integrating agile methods and principles: SCRUM, SAFe, and Kanban combined with the V-model. The methods are gradually analysed based on the literature review, and the article presents a practical application in Siemens Healthcare Slovakia.
Full article
(This article belongs to the Special Issue Advances in Software Engineering and Programming Languages)
Open AccessArticle
Study of Fixed Point Message Scheduling Algorithm for In-Vehicle Ethernet
by
Jiaoyue Chen, Qihui Zuo, Yihu Xu, Yujing Wu, Wenquan Jin and Yinan Xu
Electronics 2024, 13(11), 2050; https://doi.org/10.3390/electronics13112050 - 24 May 2024
Abstract
With the rapid development of advanced driver assistance systems (ADASs) and autonomous driving technology, in-vehicle networks are facing huge challenges in real-time operation and data loss. Traditional vehicle bus network systems such as LIN, CAN, and FlexRay are insufficient to meet the real-time
[...] Read more.
With the rapid development of advanced driver assistance systems (ADASs) and autonomous driving technology, in-vehicle networks are facing huge challenges in real-time operation and data loss. Traditional vehicle bus network systems such as LIN, CAN, and FlexRay are insufficient to meet the real-time requirements of intelligent connected vehicles. In-vehicle Ethernet meets the requirements of high reliability, low electromagnetic radiation, low power consumption, bandwidth allocation, low latency, and real-time synchronization of intelligent connected vehicles. In-vehicle Ethernet has become one of the trends in the next generation of in-vehicle network architecture. This research focuses on the delay problem existing in the real-time data transmission process of in-vehicle Ethernet, and innovatively proposes a fixed point message scheduling algorithm (FPMS) based on time-sensitive network (TSN) technology. By building an experimental platform based on the CANoe simulation tool, the high-efficiency message transmission performance of the fixed point message scheduling algorithm was verified. Experimental results show that the fixed point message scheduling algorithm proposed in this study improves message transmission efficiency by 66%, laying a solid foundation for improving the real-time and reliability performance of in-vehicle Ethernet.
Full article
Open AccessArticle
Active Learning in Feature Extraction for Glass-in-Glass Detection
by
Jerzy Rapcewicz and Marcin Malesa
Electronics 2024, 13(11), 2049; https://doi.org/10.3390/electronics13112049 - 24 May 2024
Abstract
In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection
[...] Read more.
In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection than RGB cameras, making them suitable for detecting various contaminants. However, acquiring sufficient defective samples for classification is costly and time-consuming. To address this, we propose an anomaly detection system requiring only non-defective samples, automatically classifying anything not recognized as good as defective. Our system, employing active learning on X-ray images, efficiently detects defects like glass fragments in food products. By fine tuning a feature extractor and autoencoder based on non-defective samples, our method improves classification accuracy while minimizing the need for manual intervention over time. The system achieves a 97.4% detection rate for foreign glass bodies in glass jars, offering a fast and effective solution for real-time quality control on production lines.
Full article
(This article belongs to the Special Issue Artificial Intelligence in Image Processing and Computer Vision)
►▼
Show Figures
Figure 1
Open AccessArticle
Key Issues on Integrating 5G into Industrial Systems
by
Jiadong Sun, Deji Chen, Quan Wang, Chao Lei, Mengnan Wang, Ziheng Li, Yang Xiao, Weiwei Zhang and Jiale Liu
Electronics 2024, 13(11), 2048; https://doi.org/10.3390/electronics13112048 - 24 May 2024
Abstract
Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures,
[...] Read more.
Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields.
Full article
(This article belongs to the Special Issue Recent Progress in Wireless Communication Networks)
►▼
Show Figures
Figure 1
Open AccessArticle
DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection
by
Shuhui Ding, Zhongyuan Guo, Xiaolong Chen, Xueyi Li and Fai Ma
Electronics 2024, 13(11), 2047; https://doi.org/10.3390/electronics13112047 - 24 May 2024
Abstract
The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence
[...] Read more.
The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during the beginning of product production, due to the occasional nature of defects and the associated costs. Herein, we present a novel image data augmentation method, which is used to generate a sufficient number of defect images. A Deep Convolution Generation Adversarial Network (DCGAN) model based on a Residual Block (ResB) and Hybrid Attention Mechanism (HAM) is proposed to generate massive defect images for the training of deep learning models. Based on a DCGAN, a ResB and a HAM are utilized as the generator and discriminator in a deep learning model. The Wasserstein distance with a gradient penalty is used to calculate the loss function so as to update the model training parameters and improve the quality of the generated image and the stability of the model by extracting deep image features and strengthening the important feature information. The approach is validated by generating enhanced defect image data and conducting a comparison with other methods, such as a DCGAN and WGAN-GP, on a rawhide stick experimental dataset.
Full article
(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)
►▼
Show Figures
Figure 1
Open AccessArticle
Gated Cross-Attention for Universal Speaker Extraction: Toward Real-World Applications
by
Yiru Zhang, Bijing Liu, Yong Yang and Qun Yang
Electronics 2024, 13(11), 2046; https://doi.org/10.3390/electronics13112046 - 24 May 2024
Abstract
Current target-speaker extraction (TSE) models have achieved good performance in separating target speech from highly overlapped multi-talker speech. However, in real-world applications, multi-talker speech is often sparsely overlapped, and the target speaker may be absent from the speech mixture, making it difficult for
[...] Read more.
Current target-speaker extraction (TSE) models have achieved good performance in separating target speech from highly overlapped multi-talker speech. However, in real-world applications, multi-talker speech is often sparsely overlapped, and the target speaker may be absent from the speech mixture, making it difficult for the model to extract the desired speech in such situations. To optimize models for various scenarios, universal speaker extraction has been proposed. However, current models do not distinguish between the presence or absence of the target speaker, resulting in suboptimal performance. In this paper, we propose a gated cross-attention network for universal speaker extraction. In our model, the cross-attention mechanism learns the correlation between the target speaker and the speech to determine whether the target speaker is present. Based on this correlation, the gate mechanism enables the model to focus on extracting speech when the target is present and filter out features when the target is absent. Additionally, we propose a joint loss function to evaluate both the reconstructed target speech and silence. Experiments on the WSJ0-2mix-extr and LibriMix datasets show that our proposed method achieves superior performance over comparison approaches in terms of SI-SDR and WER.
Full article
(This article belongs to the Special Issue Recent Advances in Audio, Speech and Music Processing and Analysis)
►▼
Show Figures
Figure 1
Open AccessArticle
Optimization of Integrated Energy Systems Based on Two-Step Decoupling Method
by
Linyang Zhang, Jianxiang Guo, Xinran Yu, Gang Hui, Na Liu, Dongdong Ren and Jijin Wang
Electronics 2024, 13(11), 2045; https://doi.org/10.3390/electronics13112045 - 24 May 2024
Abstract
An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very
[...] Read more.
An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO2 and NOX for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level.
Full article
(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
The Power Board of the KM3NeT Digital Optical Module: Design, Upgrade, and Production
by
Sebastiano Aiello, Arnauld Albert, Sergio Alves Garre, Zineb Aly, Antonio Ambrosone, Fabrizio Ameli, Michel Andre, Eleni Androutsou, Mancia Anguita, Laurent Aphecetche, Miguel Ardid, Salva Ardid, Hicham Atmani, Julien Aublin, Francesca Badaracco, Louis Bailly-Salins, Zuzana Bardacova, Bruny Baret, Adriana Bariego, Suzan Basegmez Du Pree, Yvonne Becherini, Meriem Bendahman, Francesco Benfenati, Marouane Benhassi, David M. Benoit, Edward Berbee, Vincent Bertin, Simone Biagi, Markus Boettcher, Danilo Bonanno, Jihad Boumaaza, Mohammed Bouta, Mieke Bouwhuis, Cristiano Bozza, Riccardo Maria Bozza, Horea Branzas, Felix Bretaudeau, Ronald Bruijn, Jurgen Brunner, Riccardo Bruno, Ernst Jan Buis, Raffaele Buompane, Jose Busto, Barbara Caiffi, David Calvo, Stefano Campion, Antonio Capone, Francesco Carenini, Víctor Carretero, Théophile Cartraud, Paolo Castaldi, Vincent Cecchini, Silvia Celli, Luc Cerisy, Mohamed Chabab, Michael Chadolias, Cèdric Champion, Andrew Chen, Silvio Cherubini, Tommaso Chiarusi, Marco Circella, Rosanna Cocimano, João Coelho, Alexis Coleiro, Stephane Colonges, Rosa Coniglione, Paschal Coyle, Alexandre Creusot, Giacomo Cuttone, Richard Dallier, Yara Darras, Antonio De Benedittis, Maarten de Jong, Paul de Jong, Bianca De Martino, Els de Wolf, Valentin Decoene, Riccardo Del Burgo, Ilaria Del Rosso, Umberto Maria Di Cerbo, Letizia Stella Di Mauro, Irene Di Palma, Antonio Diaz, Cristian Díaz Martín, Dídac Diego-Tortosa, Carla Distefano, Alba Domi, Corinne Donzaud, Damien Dornic, Manuel Dörr, Evangelia Drakopoulou, Doriane Drouhin, Rastislav Dvornický, Thomas Eberl, Eliska Eckerova, Ahmed Eddymaoui, Maximilian Eff, Imad El Bojaddaini, Sonia El Hedri, Alexander Enzenhöfer, Giovanna Ferrara, Miroslav Filipovic, Francesco Filippini, Dino Franciotti, Luigi Antonio Fusco, Omar Gabella, Jean-Louis Gabriel, Silvia Gagliardini, Tamas Gal, Juan García Méndez, Alfonso Andres Garcia Soto, Clara Gatius Oliver, Nicole Geißelbrecht, Houria Ghaddari, Lucio Gialanella, Brad K. Gibson, Emidio Giorgio, Isabel Goos, Pranjupriya Goswami, Damien Goupilliere, Sara Rebecca Gozzini, Rodrigo Gracia, Kay Graf, Carlo Guidi, Benoît Guillon, Miguel Gutiérrez, Aart Heijboer, Amar Hekalo, Lukas Hennig, Juan-Jose Hernandez-Rey, Walid Idrissi Ibnsalih, Giulia Illuminati, Peter Jansweijer, Bouke Jisse Jung, Piotr Kalaczyński, Oleg Kalekin, Uli Katz, Amina Khatun, Giorgi Kistauri, Claudio Kopper, Antoine Kouchner, Vincent Kueviakoe, Vladimir Kulikovskiy, Ramaz Kvatadze, Marc Labalme, Robert Lahmann, Giuseppina Larosa, Chiara Lastoria, Alfonso Lazo, Sebastien Le Stum, Grégory Lehaut, Emanuele Leonora, Nadja Lessing, Giuseppe Levi, Miles Lindsey Clark, Pietro Litrico, Fabio Longhitano, Jerzy Mańczak, Jhilik Majumdar, Leonardo Malerba, Fadahat Mamedov, Alberto Manfreda, Martina Marconi, Annarita Margiotta, Antonio Marinelli, Christos Markou, Lilian Martin, Juan Antonio Martínez-Mora, Fabio Marzaioli, Massimo Mastrodicasa, Stefano Mastroianni, Sandra Miccichè, Gennaro Miele, Pasquale Migliozzi, Emilio Migneco, Saverio Minutoli, Maria Lucia Mitsou, Carlos Maximiliano Mollo, Lizeth Morales Gallegos, Michele Morga, Abdelilah Moussa, Ivan Mozun Mateo, Rasa Muller, Paolo Musico, Maria Rosaria Musone, Mario Musumeci, Sergio Navas, Amid Nayerhoda, Carlo Alessandro Nicolau, Bhuti Nkosi, Brían Ó Fearraigh, Veronica Oliviero, Angelo Orlando, Enzo Oukacha, Daniele Paesani, Juan Palacios González, Gogita Papalashvili, Vittorio Parisi, Emilio Pastor, Alice Paun, Gabriela Emilia Pavalas, Giuliano Pellegrini, Santiago Pena Martinez, Mathieu Perrin-Terrin, Jerome Perronnel, Valentin Pestel, Rebekah Pestes, Paolo Piattelli, Chiara Poirè, Vlad Popa, Thierry Pradier, Jorge Prado, Sara Pulvirenti, Gilles Quemener, Carlos Quiroz, Ushak Rahaman, Nunzio Randazzo, Richard Randriatoamanana, Soebur Razzaque, Immacolata Carmen Rea, Diego Real, Giorgio Riccobene, Joshua Robinson, Andrey Romanov, Adrian Saina, Francisco Salesa Greus, Dorothea Franziska Elisabeth Samtleben, Agustín Sánchez Losa, Simone Sanfilippo, Matteo Sanguineti, Claudio Santonastaso, Domenico Santonocito, Piera Sapienza, Jan-Willem Schmelling, Jutta Schnabel, Johannes Schumann, Hester Schutte, Jordan Seneca, Nour-Eddine Sennan, Bastian Setter, Irene Sgura, Rezo Shanidze, Ankur Sharma, Yury Shitov, Fedor Šimkovic, Andreino Simonelli, Anna Sinopoulou, Mikhail Smirnov, Bernardino Spisso, Maurizio Spurio, Dimitris Stavropoulos, Ivan Štekl, Mauro Taiuti, Yahya Tayalati, Hannes Thiersen, Iara Tosta e Melo, Efi Tragia, Benjamin Trocme, Vasileios Tsourapis, Ekaterini Tzamariudaki, Antonin Vacheret, Angel Valer Melchor, Veronica Valsecchi, Vincent van Beveren, Thijs van Eeden, Daan van Eijk, Véronique Van Elewyck, Hans van Haren, Godefroy Vannoye, George Vasileiadis, Francisco Vazquez De Sola, Cedric Verilhac, Alessandro Veutro, Salvatore Viola, Daniele Vivolo, Joern Wilms, Harold Yepes Ramirez, Giorgos Zarpapis, Sandra Zavatarelli, Angela Zegarelli, Daniele Zito, Juan de Dios Zornoza, Juan Zuñiga and Natalia Zywuckaadd
Show full author list
remove
Hide full author list
Electronics 2024, 13(11), 2044; https://doi.org/10.3390/electronics13112044 - 24 May 2024
Abstract
►▼
Show Figures
The KM3NeT Collaboration is building an underwater neutrino observatory at the bottom of the Mediterranean Sea, consisting of two neutrino telescopes, both composed of a three-dimensional array of light detectors, known as digital optical modules. Each digital optical module contains a set of
[...] Read more.
The KM3NeT Collaboration is building an underwater neutrino observatory at the bottom of the Mediterranean Sea, consisting of two neutrino telescopes, both composed of a three-dimensional array of light detectors, known as digital optical modules. Each digital optical module contains a set of 31 three-inch photomultiplier tubes distributed over the surface of a 0.44 m diameter pressure-resistant glass sphere. The module also includes calibration instruments and electronics for power, readout, and data acquisition. The power board was developed to supply power to all the elements of the digital optical module. The design of the power board began in 2013, and ten prototypes were produced and tested. After an exhaustive validation process in various laboratories within the KM3NeT Collaboration, a mass production batch began, resulting in the construction of over 1200 power boards so far. These boards were integrated in the digital optical modules that have already been produced and deployed, which total 828 as of October 2023. In 2017, an upgrade of the power board, to increase reliability and efficiency, was initiated. The validation of a pre-production series has been completed, and a production batch of 800 upgraded boards is currently underway. This paper describes the design, architecture, upgrade, validation, and production of the power board, including the reliability studies and tests conducted to ensure safe operation at the bottom of the Mediterranean Sea throughout the observatory’s lifespan.
Full article
Figure 1
Open AccessArticle
Assessing the Effects of Various Gaming Platforms on Players’ Affective States and Workloads through Electroencephalogram
by
Pratheep Kumar Paranthaman, Spencer Graham and Nikesh Bajaj
Electronics 2024, 13(11), 2043; https://doi.org/10.3390/electronics13112043 - 23 May 2024
Abstract
Game platforms have different impacts on player experience in terms of affective states and workloads. By studying these impacts, we can uncover detailed aspects of the gaming experience. Traditionally, understanding player experience has relied on subjective methods, such as self-reported surveys, where players
[...] Read more.
Game platforms have different impacts on player experience in terms of affective states and workloads. By studying these impacts, we can uncover detailed aspects of the gaming experience. Traditionally, understanding player experience has relied on subjective methods, such as self-reported surveys, where players reflect on their experience and effort levels. However, complementing these subjective measures with electroencephalogram (EEG) analysis introduces an objective approach to assessing player experience. In this study, we examined player experiences across PlayStation 5, Nintendo Switch, and Meta Quest 2. Using a mixed-methods approach, we merged subjective user assessments with EEG data to investigate brain activity, affective states, and workload during low- and high-stimulation games. We recruited 30 participants to play two games across three platforms. Our findings reveal that there is a statistically significant difference between these three platforms for seven out of nine experience factors. Also, three platforms have different impacts on play experience and brain activity. Additionally, we utilized a linear model to associate player experience aspects such arousal, frustration, and mental workload with different brain regions using EEG data.
Full article
(This article belongs to the Special Issue Recent Advances in Extended Reality)
Open AccessArticle
Overcoming Fear and Improving Public Speaking Skills through Adaptive VR Training
by
Nicolae Jinga, Ana Magdalena Anghel, Florica Moldoveanu, Alin Moldoveanu, Anca Morar and Livia Petrescu
Electronics 2024, 13(11), 2042; https://doi.org/10.3390/electronics13112042 - 23 May 2024
Abstract
This paper examines the effectiveness of virtual reality (VR) in training public speaking skills. The fear of public speaking (FPS) is a common problem that can have a significant impact on an individual’s professional and personal life. Traditional therapies for public speaking anxiety
[...] Read more.
This paper examines the effectiveness of virtual reality (VR) in training public speaking skills. The fear of public speaking (FPS) is a common problem that can have a significant impact on an individual’s professional and personal life. Traditional therapies for public speaking anxiety have been shown to be effective, but there is growing interest in the use of VR as an alternative or supplement to these therapies. This study aims to investigate the VR medium in improving public speaking skills and to explore the potential mechanisms underlying this effect. A framework was developed with the aim to investigate the possibility of improving public speaking skills through VR. Key features of this framework include the ability to adjust the audience size and alter the dimensions of the room. Additionally, it allows for the modification of initial audience behaviors. One of the innovative aspects is the inclusion of an evolving attention span in the virtual audience, adding a dynamic element to the VR experience. The framework excels in tracking various metrics in real time and has the audience react dynamically based on them. These metrics include movement and voice parameters. The system is designed to present this data as immediate feedback to the user, but also as a summary after a presentation has concluded. After an extensive two-phased testing, the results are discussed. These findings suggest that VR can be an effective means for improving public speaking skills and potentially helping in alleviating fear of public speaking.
Full article
Open AccessArticle
Using the Buckingham π Theorem for Multi-System Transfer Learning: A Case-Study with 3 Vehicles Sharing a Database
by
William Therrien, Olivier Lecompte and Alexandre Girard
Electronics 2024, 13(11), 2041; https://doi.org/10.3390/electronics13112041 - 23 May 2024
Abstract
Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very
[...] Read more.
Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very useful, especially for vehicles difficult to model analytically, for instance, ground vehicles for which the ground-tire interaction is hard to model from first principles. However, learning schemes are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of an approach that uses dimensionless numbers based on Buckingham’s theorem to improve the efficiency of data for learning models, with the goal of facilitating knowledge sharing between similar systems. A case study using car-like vehicles compares traditional and dimensionless models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results from the case study presented show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model prediction when transferring the learned model between various similar vehicles. Prediction accuracy improvements with the dimensionless scheme when using a shared database, that is, predicting the motion of a vehicle based on data from various different vehicles was found to be 480% more accurate for predicting a simple no-slip maneuver based on simulated data and 11% more accurate to predict a highly dynamic braking maneuver based on experimental data. A modified physics-informed learning scheme with hand-crafted dimensionless features was also shown to increase the improvement to precision gains of 917% and 28% respectively. A comparative study also shows that using Buckingham’s theorem is a much more effective preprocessing step for this task than principal component analysis (PCA) or simply normalizing the data. These results show that the use of dimensionless variables is a promising tool to help in the task of learning a more generalizable motion model for vehicles, and hence potentially taking advantage of the data generated by fleets of vehicles on the road even though they are not identical.
Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
Open AccessArticle
Multi-Modular Network-Based Retinex Fusion Approach for Low-Light Image Enhancement
by
Jiarui Wang, Yu Sun and Jie Yang
Electronics 2024, 13(11), 2040; https://doi.org/10.3390/electronics13112040 - 23 May 2024
Abstract
Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization
[...] Read more.
Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization network for enhancing low-light images by integrating deep learning with Retinex theory. First, we create a decomposition network to separate the lighting components and reflections from the low-light image. We incorporated an enhanced global spatial attention (GSA) module into the decomposition network to boost its flexibility and adaptability. This module enhances the extraction of comprehensive information from the image and safeguards against information loss. To increase the illumination component’s luminosity, we subsequently constructed an enhancement network. The Multiscale Guidance Block (MSGB) has been integrated into the improvement network, together with multilayer extended convolution to expand the sensing field and enhance the network’s capability for feature extraction. Our proposed method out-performs existing ways in both objective measures and personal evaluations, emphasizing the virtues of the procedure outlined in this paper.
Full article
(This article belongs to the Special Issue Modern Computer Vision and Image Analysis)
Open AccessArticle
Video Summarization Generation Network Based on Dynamic Graph Contrastive Learning and Feature Fusion
by
Jing Zhang, Guangli Wu, Xinlong Bi and Yulong Cui
Electronics 2024, 13(11), 2039; https://doi.org/10.3390/electronics13112039 - 23 May 2024
Abstract
Video summarization aims to analyze the structure and content of videos and extract key segments to construct summarization that can accurately summarize the main content, allowing users to quickly access the core information without browsing the full video. However, existing methods have difficulties
[...] Read more.
Video summarization aims to analyze the structure and content of videos and extract key segments to construct summarization that can accurately summarize the main content, allowing users to quickly access the core information without browsing the full video. However, existing methods have difficulties in capturing long-term dependencies when dealing with long videos. On the other hand, there is a large amount of noise in graph structures, which may lead to the influence of redundant information and is not conducive to the effective learning of video features. To solve the above problems, we propose a video summarization generation network based on dynamic graph contrastive learning and feature fusion, which mainly consists of three modules: feature extraction, video encoder, and feature fusion. Firstly, we compute the shot features and construct a dynamic graph by using the shot features as nodes of the graph and the similarity between the shot features as the weights of the edges. In the video encoder, we extract the temporal and structural features in the video using stacked L-G Blocks, where the L-G Block consists of a bidirectional long short-term memory network and a graph convolutional network. Then, the shallow-level features are obtained after processing by L-G Blocks. In order to remove the redundant information in the graph, graph contrastive learning is used to obtain the optimized deep-level features. Finally, to fully exploit the feature information of the video, a feature fusion gate using the gating mechanism is designed to fully fuse the shallow-level features with the deep-level features. Extensive experiments are conducted on two benchmark datasets, TVSum and SumMe, and the experimental results show that our proposed method outperforms most of the current state-of-the-art video summarization methods.
Full article
(This article belongs to the Section Artificial Intelligence)
Open AccessArticle
Context Awareness Assisted Integration System for Land Vehicles
by
Xiaoyu Li, Xiye Guo, Kai Liu, Zhijun Meng, Guokai Chen, Yuqiu Tang and Jun Yang
Electronics 2024, 13(11), 2038; https://doi.org/10.3390/electronics13112038 - 23 May 2024
Abstract
Accurate context awareness of land vehicles can assist integrated navigation systems. Motion behavior recognition is one context awareness of vehicles, based on which constraint information helps reduce the impact of short-term blockage of navigation signals on radio-frequency-based positioning systems. To improve the
[...] Read more.
Accurate context awareness of land vehicles can assist integrated navigation systems. Motion behavior recognition is one context awareness of vehicles, based on which constraint information helps reduce the impact of short-term blockage of navigation signals on radio-frequency-based positioning systems. To improve the reliability of behavior recognition, we proposed a machine learning-based vehicle motion behavior recognition and constraint method (MLMRC). The machine learning-based recognition process is directly driven by raw data from low-cost MEMS-IMU, while the traditional threshold-based method relies on previous experience. Four categories of constraint information—sensor error calibration, velocity constraint, angle constraint, and position constraint—were constructed from the recognition results. Both the simulated vehicle experiment and real vehicle experiment demonstrate the performance of the MLMRC method. When there is a short-term blockage, the MLMRC method can reduce the positioning error from 17.2% to 38.3% compared with the traditional method, which effectively improves positioning accuracy and provides support for autonomous vehicles in complex urban environments.
Full article
(This article belongs to the Special Issue Advancements in Sensing and Perception for Autonomous Vehicles in Adverse Environmental Conditions)
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
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
Topic in
Drones, Electronics, Future Internet, Information, Mathematics
Future Internet Architecture: Difficulties and Opportunities
Topic Editors: Peiying Zhang, Haotong Cao, Keping YuDeadline: 30 June 2024
Topic in
Applied Sciences, Electronics, Photonics, Remote Sensing, Technologies
Emerging Terahertz Technologies for Integrated Sensing and Communication
Topic Editors: Jianjun Ma, Xiue Bao, Bin Li, Suman MukherjeeDeadline: 31 July 2024
Conferences
Special Issues
Special Issue in
Electronics
Advances in Swarm Intelligence, Data Science and Their Applications, 2nd Edition
Guest Editor: Ying TanDeadline: 25 May 2024
Special Issue in
Electronics
Network Intrusion Detection Using Deep Learning
Guest Editor: Harald VrankenDeadline: 31 May 2024
Special Issue in
Electronics
Satellite-Terrestrial Integrated Internet of Things
Guest Editors: Min Jia, Zhenyu Na, Xin Liu, Lexi XuDeadline: 15 June 2024
Special Issue in
Electronics
Modeling and Optimization of Energy Efficiency in the Light of Energy Security
Guest Editors: Aurelia Rybak, Aleksandra Rybak, Jarosław JoostberensDeadline: 1 July 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