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
Digitally Controlled Fractional-Order Elements Using OTA-C Structures
Electronics 2024, 13(11), 2066; https://doi.org/10.3390/electronics13112066 (registering DOI) - 26 May 2024
Abstract
This article presents an active realisation of an electronically controlled FO capacitor or a constant phase element (CPE) and an FO inductor (FOI) in the form of an integrated circuit. The realisation is demonstrated using an OTA-C structure in AMS 0.35 μm C35B4C3
[...] Read more.
This article presents an active realisation of an electronically controlled FO capacitor or a constant phase element (CPE) and an FO inductor (FOI) in the form of an integrated circuit. The realisation is demonstrated using an OTA-C structure in AMS 0.35 μm C35B4C3 technology. The same core is used for both realisations of CPE and FOI, and the angles can be realised in all four quadrants. The realisation of active constant-phase elements using OTAs with MOS transistors in the saturation region is proposed. The operating frequency is in the high range of 7–350 kHz, with a centre frequency of 50 kHz. A tuning method is proposed using different bias currents of the OTAs, which in turn are digitally controlled to obtain the desired parameters such as impedance and angle of an element. The linearisation of the individual OTAs is achieved by source degeneration. The newly introduced minimax approximation is used to design three non-integer orders of 1/3, 1/2, and 2/3. The integrated circuit was designed with a total area of 710 × 1127 µm2. The power consumption of the entire system is 12.37 mW.
Full article
(This article belongs to the Special Issue CMOS Integrated Circuits Design)
►
Show Figures
Open AccessFeature PaperArticle
A Study on an IoT-Based SCADA System for Photovoltaic Utility Plants
by
Sergio Ferlito, Salvatore Ippolito, Celestino Santagata, Paolo Schiattarella and Girolamo Di Francia
Electronics 2024, 13(11), 2065; https://doi.org/10.3390/electronics13112065 (registering DOI) - 26 May 2024
Abstract
Large-scale photovoltaic (PV) electricity production plants rely on reliable operation and maintenance (O&M) systems, often operated by means of supervisory control and data acquisition (SCADA) platforms aimed at limiting, as much as possible, the intrinsic volatility of this energy resource. The current trend
[...] Read more.
Large-scale photovoltaic (PV) electricity production plants rely on reliable operation and maintenance (O&M) systems, often operated by means of supervisory control and data acquisition (SCADA) platforms aimed at limiting, as much as possible, the intrinsic volatility of this energy resource. The current trend is to develop SCADAs that achieve the finest possible control of the system components to efficiently and effectively cope with possible energy delivery problems. In this study, we investigated an innovative design of an IoT-based SCADA specifically tailored for large PV systems in which data transmission overheads are reduced by adopting lightweight protocols, and reliable data storage is achieved by means of hybrid solutions that allow the storage of historical data, enabling accurate performance analysis and predictive maintenance protocols. The proposed solution relies on an architecture where independent functional microservices handle specific tasks, ensuring scalability and fault tolerance. The technical approaches for IoT-SCADA connectivity are herein described in detail, comparing different possible technical choices. The proposed IoT-based SCADA is based on edge computing for latency reduction and to enhance real-time decision making, enabling scalability, and centralized management while leveraging cloud services. The resulting hybrid solutions that combine edge and cloud resources offer a balance between responsiveness and scalability. Finally, in the study, a blockchain solution was taken into account to certify energy data, ensuring traceability, security, and reliability in commercial transactions.
Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
Open AccessFeature PaperArticle
A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems
by
Vahid Hajihashemi, Abdorreza Alavi Gharahbagh, Narges Hajaboutalebi, Mohsen Zahraei, José J. M. Machado and João Manuel R. S. Tavares
Electronics 2024, 13(11), 2064; https://doi.org/10.3390/electronics13112064 (registering DOI) - 25 May 2024
Abstract
Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of
[...] Read more.
Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of frequency bands. A big challenge is that some of the spectrogram bands may be similar in different events and be useless in AED. Removing useless bands reduces the input feature dimension and is highly desirable. This article proposes a mathematical feature analysis method to identify and eliminate ineffective spectrogram bands and improve AED systems’ efficiency. The proposed approach uses a Student’s t-test to compare frequency bands of the spectrogram from different acoustic events. The similarity between each frequency band among events is calculated using a two-sample t-test, allowing the identification of distinct and similar frequency bands. Removing these bands accelerates the training speed of the used classifier by reducing the number of features, and also enhances the system’s accuracy and efficiency. Based on the obtained results, the proposed method reduces the spectrogram bands by 26.3%. The results showed an average difference of 7.77% in the Jaccard, 4.07% in the Dice, and 5.7% in the Hamming distance between selected bands using train and test datasets. These small values underscore the validity of the obtained results for the test dataset.
Full article
(This article belongs to the Special Issue Recent Advances in Audio, Speech and Music Processing and Analysis)
Open AccessArticle
Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification
by
Yichen Xu, Liang Huang, Linghong Zhang, Liping Qian and Xiaoniu Yang
Electronics 2024, 13(11), 2063; https://doi.org/10.3390/electronics13112063 (registering DOI) - 25 May 2024
Abstract
Deep learning has become a powerful tool for automatically classifying modulations in received radio signals, a task traditionally reliant on manual expertise. However, the effectiveness of deep learning models hinges on the availability of substantial data. Limited training data often results in overfitting,
[...] Read more.
Deep learning has become a powerful tool for automatically classifying modulations in received radio signals, a task traditionally reliant on manual expertise. However, the effectiveness of deep learning models hinges on the availability of substantial data. Limited training data often results in overfitting, which significantly impacts classification accuracy. Traditional signal augmentation methods like rotation and flipping have been employed to mitigate this issue, but their effectiveness in enriching datasets is somewhat limited. This paper introduces the Diffusion-based Radio Signal Augmentation algorithm (DiRSA), a novel signal augmentation method that significantly enhances dataset scale without compromising signal integrity. Utilizing prompt words for precise signal generation, DiRSA allows for flexible modulation control and significantly expands the training dataset beyond the original scale. Extensive evaluations demonstrate that DiRSA outperforms traditional signal augmentation techniques such as rotation and flipping. Specifically, when applied with the LSTM model in small dataset scenarios, DiRSA enhances modulation classification performance at SNRs above 0 dB by 6%.
Full article
(This article belongs to the Special Issue Recent Advances of Cloud, Edge, and Parallel Computing)
Open AccessArticle
Self-Knowledge Distillation via Progressive Associative Learning
by
Haoran Zhao, Yanxian Bi, Shuwen Tian, Jian Wang, Peiying Zhang, Zhaopeng Deng and Kai Liu
Electronics 2024, 13(11), 2062; https://doi.org/10.3390/electronics13112062 (registering DOI) - 25 May 2024
Abstract
As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture
[...] Read more.
As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network.
Full article
(This article belongs to the Topic Future Internet Architecture: Difficulties and Opportunities)
Open AccessArticle
LCV2: A Universal Pretraining-Free Framework for Grounded Visual Question Answering
by
Yuhan Chen, Lumei Su, Lihua Chen and Zhiwei Lin
Electronics 2024, 13(11), 2061; https://doi.org/10.3390/electronics13112061 (registering DOI) - 25 May 2024
Abstract
Grounded Visual Question Answering systems place heavy reliance on substantial computational power and data resources in pretraining. In response to this challenge, this paper introduces the LCV2 modular approach, which utilizes a frozen large language model (LLM) to bridge the off-the-shelf generic visual
[...] Read more.
Grounded Visual Question Answering systems place heavy reliance on substantial computational power and data resources in pretraining. In response to this challenge, this paper introduces the LCV2 modular approach, which utilizes a frozen large language model (LLM) to bridge the off-the-shelf generic visual question answering (VQA) module with a generic visual grounding (VG) module. It leverages the generalizable knowledge of these expert models, avoiding the need for any large-scale pretraining. Innovatively, within the LCV2 framework, question and predicted answer pairs are transformed into descriptive and referring captions, enhancing the clarity of the visual cues directed by the question text for the VG module’s grounding. This compensates for the limitations of missing intrinsic text–visual coupling in non-end-to-end frameworks. Comprehensive experiments on benchmark datasets, such as GQA, CLEVR, and VizWiz-VQA-Grounding, were conducted to evaluate the method’s performance and compare it with several baseline methods. In particular, it achieved an IoU F1 score of 59.6% on the GQA dataset and an IoU F1 score of 37.4% on the CLEVR dataset, surpassing some baseline results and demonstrating the LCV2’s competitive performance.
Full article
(This article belongs to the Special Issue Advances in Large Language Model Empowered Machine Learning: Design and Application)
Open AccessArticle
Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering
by
Ming Dong, Yujuan Lu, Zhenhua Tan and Bin Zhang
Electronics 2024, 13(11), 2060; https://doi.org/10.3390/electronics13112060 (registering DOI) - 25 May 2024
Abstract
This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this
[...] Read more.
This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators.
Full article
Open AccessFeature PaperArticle
Current-Mode Active Filter Using EX-CCCII
by
Montree Kumngern, Fabian Khateb, Tomasz Kulej and Siraphop Tooprakai
Electronics 2024, 13(11), 2059; https://doi.org/10.3390/electronics13112059 (registering DOI) - 25 May 2024
Abstract
This paper presents a novel multiple-input and multiple-output current-mode universal analog filter with electronic tuning capability. The proposed circuit uses a single second-generation current-controlled current conveyor with extra-X terminals (EX-CCCII) and two grounded capacitors. The filter can offer five standard filtering functions, namely
[...] Read more.
This paper presents a novel multiple-input and multiple-output current-mode universal analog filter with electronic tuning capability. The proposed circuit uses a single second-generation current-controlled current conveyor with extra-X terminals (EX-CCCII) and two grounded capacitors. The filter can offer five standard filtering functions, namely low-pass, high-pass, band-pass, band-stop, all-pass responses, in the same circuit without changing the internal configuration of the filter by selecting appropriate input and output signals. To obtain the five standard filtering functions, inverted input signal and input matching conditions are absent. The natural frequency of all filter responses can be electronically controlled. The proposed circuit was simulated by SPICE using 0.18 μm CMOS process from Taiwan Semiconductor Manufacturing Company (TSMC). The results of experiments using the integrated circuit operational amplifier AD844 confirm the functionality of the new filter.
Full article
(This article belongs to the Section Circuit and Signal Processing)
►▼
Show Figures
Figure 1
Open AccessArticle
Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning
by
Bálint Kővári, Bálint Pelenczei, István Gellért Knáb and Tamás Bécsi
Electronics 2024, 13(11), 2058; https://doi.org/10.3390/electronics13112058 (registering DOI) - 25 May 2024
Abstract
In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping,
[...] Read more.
In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive training through a trial-and-error strategy across conceivable reward functions, which is a process notorious for its time-consuming nature and substantial financial implications. Contrary to conventional methodologies, the Monte Carlo Tree Search (MCTS) enables the prediction of reward function quality through Monte Carlo simulations, thereby eliminating the need for exhaustive training on all available reward functions. The findings obtained from MCTS simulations can be effectively leveraged to selectively train only the most suitable RL models. This approach helps alleviate the resource-heavy nature of traditional RL processes through altering the training pipeline. This paper validates the theoretical framework concerning the unique property of the Monte Carlo Tree Search algorithm by emphasizing its generality through highlighting crossalgorithmic and crossenvironmental capabilities while also showcasing its potential to reduce training costs.
Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
Open AccessArticle
Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture
by
Antonios Chatzisavvas, Michael Dossis and Minas Dasygenis
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)
►▼
Show Figures
Figure 1
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
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
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
Special Issue in
Electronics
Advances in Human-Machine Interaction, Artificial Intelligence, and Robotics
Guest Editors: Juan Ernesto Solanes Galbis, Luis Gracia, Jaime Valls MiroDeadline: 15 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