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
DiT-Gesture: A Speech-Only Approach to Stylized Gesture Generation
Electronics 2024, 13(9), 1702; https://doi.org/10.3390/electronics13091702 (registering DOI) - 27 Apr 2024
Abstract
The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person’s ID and emotion
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The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person’s ID and emotion for driving co-speech gesture generation. However, this endeavor still faces significant challenges. These challenges go beyond the intricate interplay among co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces “DiT-Gestures”, a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with the WavLM pre-trained model and a dynamic mask attention network (DMAN). It can produce individual and stylized full-body co-speech gestures by only using raw speech audio, eliminating the need for complex multimodal processing and manual annotation. Firstly, considering that speech audio contains acoustic and semantic features and conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract the style from raw audio information. Secondly, we replace the causal mask by introducing a learnable dynamic mask for better local modeling in the neighborhood of the target frames. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm WavLM’s and the model’s ability to synthesize natural co-speech gestures with various styles.
Full article
(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)
Open AccessFeature PaperArticle
A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment
by
Seonwoo Lee, Akeem Bayo Kareem and Jang-Wook Hur
Electronics 2024, 13(9), 1700; https://doi.org/10.3390/electronics13091700 (registering DOI) - 27 Apr 2024
Abstract
Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of
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Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.
Full article
(This article belongs to the Special Issue Current Trends on Data Management)
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Open AccessArticle
OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection
by
Jiajun Xu, Yuzhen Lu and Boyang Deng
Electronics 2024, 13(9), 1699; https://doi.org/10.3390/electronics13091699 (registering DOI) - 27 Apr 2024
Abstract
Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has
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Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.
Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image Analysis for Biological Systems)
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Open AccessArticle
Blood Cell Attribute Classification Algorithm Based on Partial Label Learning
by
Junxin Feng, Qianhang Guo, Shiling Luo, Letao Chen and Qiongxiong Ma
Electronics 2024, 13(9), 1698; https://doi.org/10.3390/electronics13091698 (registering DOI) - 27 Apr 2024
Abstract
Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces
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Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces the Partial Label Learning for Blood Cell Classification (P4BC) strategy, a method that trains neural networks using the blood cell attribute labeling data of weak annotations. Using morphological knowledge, we predefined candidate label sets for the blood cell attributes to blend this knowledge with deep learning. This improves the model’s prediction accuracy and interpretability in classifying attributes. This method effectively combines morphological knowledge with deep learning, an approach we refer to as knowledge alignment. It results in an 8.66% increase in attribute recognition accuracy and a 1.09% improvement in matching predictions to the candidate label sets, compared to the original method. These results confirm our method’s ability to grasp the characteristic information of blood cell attributes, enhancing the model interpretability and achieving knowledge alignment between hematological morphology and deep learning. Our algorithm ensures attribute classification accuracy and shows excellent cell category classification, highlighting its wide application potential and practical value in blood cell category classification.
Full article
(This article belongs to the Special Issue Advances in Image Processing and Detection)
Open AccessArticle
NRPerson: A Non-Registered Multi-Modal Benchmark for Tiny Person Detection and Localization
by
Yi Yang, Xumeng Han, Kuiran Wang, Xuehui Yu, Wenwen Yu, Zipeng Wang, Guorong Li, Zhenjun Han and Jianbin Jiao
Electronics 2024, 13(9), 1697; https://doi.org/10.3390/electronics13091697 (registering DOI) - 27 Apr 2024
Abstract
In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual
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In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements.
Full article
(This article belongs to the Special Issue Big Model Techniques for Image Processing)
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Open AccessArticle
Multi-Objective Optimization in 3D Floorplanning
by
Zhongjie Jiang, Zhiqiang Li and Zhenjie Yao
Electronics 2024, 13(9), 1696; https://doi.org/10.3390/electronics13091696 (registering DOI) - 27 Apr 2024
Abstract
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Three-dimensional integrated circuits can significantly mitigate the challenges posed by shrinking feature sizes and enable heterogeneous integration. This paper focuses on the 3D floorplanning problem. We formulate it as a multi-objective optimization issue and employ multi-objective simulated annealing to simultaneously optimize area, wirelength
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Three-dimensional integrated circuits can significantly mitigate the challenges posed by shrinking feature sizes and enable heterogeneous integration. This paper focuses on the 3D floorplanning problem. We formulate it as a multi-objective optimization issue and employ multi-objective simulated annealing to simultaneously optimize area, wirelength and number of vias. During the optimization process, neighboring solutions are explored in the design space through inter-layer or intra-layer perturbations, and decision criteria for the exploration process are formulated based on the dominance relationship of solutions. Test results on the GSRC benchmark demonstrate that our approach delivers superior performance in optimizing area and wirelength. Compared to 2D floorplanning, our method reduces the area by approximately 49% and the wirelength by 21%. Compared to other similar 3D floorplanning methods, we raise the success rate in satisfying the fixed-outline constraint to 100% and improve the wirelength by 3%. The multi-objective simulated annealing method proposed in this paper can effectively address the 3D floorplanning problem.
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Open AccessCommunication
Design Techniques for Wideband CMOS Power Amplifiers for Wireless Communications
by
Milim Lee, Junhyuk Yang, Jaeyong Lee and Changkun Park
Electronics 2024, 13(9), 1695; https://doi.org/10.3390/electronics13091695 (registering DOI) - 27 Apr 2024
Abstract
In this study, we designed a wideband CMOS power amplifier to support multi-band and multi-standard wireless communications. First, an input matching technique through LC network and a wideband design technique using a low Q-factor transformer were proposed. In addition, a design technique was
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In this study, we designed a wideband CMOS power amplifier to support multi-band and multi-standard wireless communications. First, an input matching technique through LC network and a wideband design technique using a low Q-factor transformer were proposed. In addition, a design technique was proposed to improve output matching using RC feedback. To verify the feasibility of the proposed design methodology for wideband CMOS power amplifiers, the designed power amplifier was fabricated using a 180 nm RFCMOS process. The size including all of the matching network and test pads was 1.38 × 0.90 mm2. In addition, the effectiveness of the proposed power amplifier was verified through the measured results using modulated signals of WCDMA, LTE, and 802.11n WLAN.
Full article
(This article belongs to the Special Issue Advanced RF, Microwave Engineering, and High-Power Microwave Sources)
Open AccessArticle
Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
by
Waled Albakosh, Rawad Asfour, Yas Khalil and Salam K. Khamas
Electronics 2024, 13(9), 1694; https://doi.org/10.3390/electronics13091694 (registering DOI) - 27 Apr 2024
Abstract
This paper presents a novel wideband circularly polarized millimeter-wave (mmWave) hemispherical dielectric resonator antenna (HDRA). Two distinct configurations of alumina dielectric resonator antennas (DRAs) are investigated, each featuring a different coating: the first configuration incorporates a polyimide layer, while the second involves a
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This paper presents a novel wideband circularly polarized millimeter-wave (mmWave) hemispherical dielectric resonator antenna (HDRA). Two distinct configurations of alumina dielectric resonator antennas (DRAs) are investigated, each featuring a different coating: the first configuration incorporates a polyimide layer, while the second involves a perforated alumina. Both configurations demonstrate promising characteristics, including impedance and axial ratio (AR) bandwidths of 58% and 17.7%, respectively, alongside a maximum gain of 10 dBic at 28 GHz. Leveraging additive manufacturing technology, the HDRA with the perforated coating layer is fabricated, simplifying assembly and eliminating potential air gaps between layers, thereby enhancing the overall performance. This innovative approach yields a circularly polarized (CP) HDRA suitable for Beyond 5G (B5G) communication systems. Agreement between measurements and simulations validates the efficacy of the proposed design, affirming its potential in practical applications.
Full article
(This article belongs to the Special Issue Smart Communication and Networking in the 6G Era)
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Open AccessArticle
Industry 4.0 Transformation: Analysing the Impact of Artificial Intelligence on the Banking Sector through Bibliometric Trends
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Alina Georgiana Manta, Roxana Maria Bădîrcea, Nicoleta Mihaela Doran, Gabriela Badareu, Claudia Gherțescu and Jenica Popescu
Electronics 2024, 13(9), 1693; https://doi.org/10.3390/electronics13091693 (registering DOI) - 27 Apr 2024
Abstract
The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a
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The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection.
Full article
(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)
Open AccessArticle
Function-Level Compilation Provenance Identification with Multi-Faceted Neural Feature Distillation and Fusion
by
Yang Gao, Lunjin Liang, Yifei Li, Rui Li and Yu Wang
Electronics 2024, 13(9), 1692; https://doi.org/10.3390/electronics13091692 (registering DOI) - 27 Apr 2024
Abstract
In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary
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In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary code. To this end, we present MulCPI, short for Multi-representation Fusion-based Compilation Provenance Identification, which integrates the features collected from multiple distinct intermediate representations of the binary code for better discernment of the fine-grained function-level compilation details. In particular, we devise a novel graph-oriented neural encoder improved upon the gated graph neural network by subtly introducing an attention mechanism into the neighborhood nodes’ information aggregation computation, in order to better distill the more informative features from the attributed control flow graph. By further integrating the features collected from the normalized assembly sequence with an advanced Transformer encoder, MulCPI is capable of capturing a more comprehensive set of features manifesting the multi-faceted lexical, syntactic, and structural insights of the binary code. Extensive evaluation on a public dataset comprising 854,858 unique functions demonstrates that MulCPI exceeds the performance of current leading methods in identifying the compiler family, optimization level, compiler version, and the combination of compilation settings. It achieves average accuracy rates of 99.3%, 96.4%, 90.7%, and 85.3% on these tasks, respectively. Additionally, an ablation study highlights the significance of MulCPI’s core designs, validating the efficiency of the proposed attention-enhanced gated graph neural network encoder and the advantages of incorporating multiple code representations.
Full article
(This article belongs to the Special Issue Machine Learning (ML) and Software Engineering, Volume II)
Open AccessArticle
Polymorphic Clustering and Approximate Masking Framework for Fine-Grained Insect Image Classification
by
Hua Huo, Aokun Mei and Ningya Xu
Electronics 2024, 13(9), 1691; https://doi.org/10.3390/electronics13091691 (registering DOI) - 27 Apr 2024
Abstract
Insect diversity monitoring is crucial for biological pest control in agriculture and forestry. Modern monitoring of insect species relies heavily on fine-grained image classification models. Fine-grained image classification faces challenges such as small inter-class differences and large intra-class variances, which are even more
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Insect diversity monitoring is crucial for biological pest control in agriculture and forestry. Modern monitoring of insect species relies heavily on fine-grained image classification models. Fine-grained image classification faces challenges such as small inter-class differences and large intra-class variances, which are even more pronounced in insect scenes where insect species often exhibit significant morphological differences across multiple life stages. To address these challenges, we introduce segmentation and clustering operations into the image classification task and design a novel network model training framework for fine-grained classification of insect images using multi-modality clustering and approximate mask methods, named PCAM-Frame. In the first stage of the framework, we adopt the Polymorphic Clustering Module, and segmentation and clustering operations are employed to distinguish various morphologies of insects at different life stages, allowing the model to differentiate between samples at different life stages during training. The second stage consists of a feature extraction network, called Basenet, which can be any mainstream network that performs well in fine-grained image classification tasks, aiming to provide pre-classification confidence for the next stage. In the third stage, we apply the Approximate Masking Module to mask the common attention regions of the most likely classes and continuously adjust the convergence direction of the model during training using a Deviation Loss function. We apply PCAM-Frame with multiple classification networks as the Basenet in the second stage and conduct extensive experiments on the Insecta dataset of iNaturalist 2017 and IP102 dataset, achieving improvements of 2.2% and 1.4%, respectively. Generalization experiments on other fine-grained image classification datasets such as CUB200-2011 and Stanford Dogs also demonstrate positive effects. These experiments validate the pertinence and effectiveness of our framework PCAM-Frame in fine-grained image classification tasks under complex conditions, particularly in insect scenes.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Image and Video Processing)
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Open AccessArticle
ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)
by
Hongkoo Kang and Sungryong Do
Electronics 2024, 13(9), 1690; https://doi.org/10.3390/electronics13091690 (registering DOI) - 26 Apr 2024
Abstract
Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to
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Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.
Full article
(This article belongs to the Special Issue Software Analysis, Quality, and Security)
Open AccessFeature PaperArticle
Characterization of Unit Cells of a Reconfigurable Intelligence Surface Integrated with Sensing Capability at the mmWave Frequency Band
by
Biswarup Rana, Sung-Sil Cho and Ic-Pyo Hong
Electronics 2024, 13(9), 1689; https://doi.org/10.3390/electronics13091689 (registering DOI) - 26 Apr 2024
Abstract
Integrated sensing and communication (ISAC) is emerging as a main feature for 5G/6G communications. To enhance spectral and energy efficiencies in wireless environments, reconfigurable intelligent surfaces (RISs) will play a significant role in beyond-5G/6G communications. Multi-functional RISs, capable of not only reflecting or
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Integrated sensing and communication (ISAC) is emerging as a main feature for 5G/6G communications. To enhance spectral and energy efficiencies in wireless environments, reconfigurable intelligent surfaces (RISs) will play a significant role in beyond-5G/6G communications. Multi-functional RISs, capable of not only reflecting or transmitting the beam in desired directions but also sensing the signal, wirelessly transferring power to nearby devices, harvesting energy, etc., will be highly beneficial for beyond-5G/6G applications. In this paper, we propose a nearly 2-bit unit cell of RISs integrated with sensing capabilities in the millimeter wave (mmWave) frequency band. To collect a very small fraction of the impinging signals through vias, we employed substrate integrated waveguide (SIW) technology at the bottom of the unit cell and a via. This enabled the sensing of incoming signals, requiring only a small amount of the impinging signal to be collected through SIW. Initially, we utilized Floquet ports and boundary conditions to obtain various parameters of the unit cells. Subsequently, we examined 1 × 3-unit cells, placing them on the waveguide model to obtain the required parameters of the unit cell. By using the waveguide and 1 × 3-unit cell arrays, the sensing amount was also determined.
Full article
(This article belongs to the Special Issue Trends and Prospects in 6G Wireless Communication)
Open AccessArticle
Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks
by
Qingyu Bie, Yuhan Zhang, Yufeng He and Yilin Lin
Electronics 2024, 13(9), 1688; https://doi.org/10.3390/electronics13091688 (registering DOI) - 26 Apr 2024
Abstract
Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers
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Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.
Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
Open AccessArticle
The Use of Technology Assisted by Artificial Intelligence Depending on the Companies’ Digital Maturity Level
by
Gabriel Brătucu, Eliza Ciobanu, Ioana Bianca Chițu, Adriana Veronica Litră, Alexandra Zamfirache and Marius Bălășescu
Electronics 2024, 13(9), 1687; https://doi.org/10.3390/electronics13091687 (registering DOI) - 26 Apr 2024
Abstract
Major companies in the global market have made significant investments in artificial intelligence-assisted technology to increase the value of their products and services, which gives the implementation of artificial intelligence an extremely important role. Starting from these premises, the authors set out to
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Major companies in the global market have made significant investments in artificial intelligence-assisted technology to increase the value of their products and services, which gives the implementation of artificial intelligence an extremely important role. Starting from these premises, the authors set out to evaluate the transformation level of companies in terms of adopting technology based on artificial intelligence according to their level of digital maturity. For this purpose, qualitative research was used by deploying the inductive method, which allowed five distinct categories of companies with unique characteristics to be identified, generating an interval scale that illustrates the level of digital maturity and the ability to adopt and implement viable solutions based on artificial intelligence technology. This paper, in addition to identifying the digital transformation level of companies, offers solutions and recommendations for addressing the challenges encountered by the business environment, thus contributing to the understanding and development of strategies adapted to each situation that may appear on the market.
Full article
Open AccessArticle
Constant-Voltage and Constant-Current Controls of the Inductive Power Transfer System for Electric Vehicles Based on Full-Bridge Synchronous Rectification
by
Jin Cai, Pan Sun, Kai Ji, Xusheng Wu, Hang Ji, Yuxiao Wang and Enguo Rong
Electronics 2024, 13(9), 1686; https://doi.org/10.3390/electronics13091686 - 26 Apr 2024
Abstract
When an inductive power transfer (IPT) system conducts wireless charging for electric cars, the coupling coefficient between the coils is easily affected by fluctuations in the external environment. With frequent changes in the battery load impedance, it is difficult for the IPT system
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When an inductive power transfer (IPT) system conducts wireless charging for electric cars, the coupling coefficient between the coils is easily affected by fluctuations in the external environment. With frequent changes in the battery load impedance, it is difficult for the IPT system to achieve constant-voltage and constant-current (CVCC) controls. A CVCC control method is proposed for the IPT system that has a double-sided LCC compensation structure based on full-bridge synchronous rectification. The proposed method achieved good dynamic stability and was able to effectively switch between the output current and voltage of the system by adjusting only the duty cycle of the switch on the secondary side of the rectification bridge. As a result, the system efficiency was improved. The output characteristics of the double-sided LCC compensation structure was derived and the conduction condition with zero voltage was analyzed by using four switches through two conduction time series of the rectifier circuit. Then, the output voltage of the synchronized rectifier was derived. The hardware implementation of the full-bridge controllable rectifier was described in detail. Finally, a MATLAB/Simulink 2018a simulation model was developed and applied to an 11 kW prototype to analyze and validate the design. The results showed that the designed system had good CVCC output characteristics and could maintain constant output under certain coupling offsets. Compared with semi synchronous rectification methods, the proposed method had a higher efficiency, which was 95.6% at the rated load.
Full article
(This article belongs to the Special Issue Recent Advances in High-Performance Wireless Power Transfer Technologies)
Open AccessArticle
An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation
by
Shengjie Ma, Pei Wang and Hyukjoon Lee
Electronics 2024, 13(9), 1685; https://doi.org/10.3390/electronics13091685 - 26 Apr 2024
Abstract
Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple
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Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.
Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
Open AccessArticle
Nonlinear Modeling and Control Strategy Based on Type-II T-S Fuzzy in Bi-Directional DC-AC Converter
by
Zhihua Chen, Ruochen Huang, Qiongbin Lin, Xinhong Yu and Zhimin Dan
Electronics 2024, 13(9), 1684; https://doi.org/10.3390/electronics13091684 - 26 Apr 2024
Abstract
Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid
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Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid waveform control. In this paper, a modeling method for bi-directional DC-AC grid-connected converters based on type-II T-S fuzzy models is proposed, and the corresponding type-II T-S fuzzy control strategy is designed to address the parameter uncertainty and non-linearity issues. Simulation results show that type-II T-S fuzzy control offers superior control performance and better current waveform quality compared to type-I T-S fuzzy control under uncertainty parameter conditions. The effectiveness of the proposed strategy is further validated through a 1 kW prototype of a bi-directional DC-AC converter.
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(This article belongs to the Special Issue Recent Advances in Electrified Vehicles and Transportation Electrification)
Open AccessReview
Neurogaming in Virtual Reality: A Review of Video Game Genres and Cognitive Impact
by
Jesus GomezRomero-Borquez, Carolina Del-Valle-Soto, J. Alberto Del-Puerto-Flores, Ramon A. Briseño and José Varela-Aldás
Electronics 2024, 13(9), 1683; https://doi.org/10.3390/electronics13091683 - 26 Apr 2024
Abstract
This work marks a significant advancement in the field of cognitive science and gaming technology. It offers an in-depth analysis of the effects of various video game genres on brainwave patterns and concentration levels in virtual reality (VR) settings. The study is groundbreaking
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This work marks a significant advancement in the field of cognitive science and gaming technology. It offers an in-depth analysis of the effects of various video game genres on brainwave patterns and concentration levels in virtual reality (VR) settings. The study is groundbreaking in its approach, employing electroencephalograms (EEGs) to explore the neural correlates of gaming, thus bridging the gap between technology, psychology, and neuroscience. This review enriches the dialogue on the potential of video games as a therapeutic tool in mental health. The study’s findings illuminate the capacity of different game genres to elicit varied brainwave responses, paving the way for tailored video game therapies. This review contributes meaningfully to the state of the art by offering empirical insights into the interaction between gaming environments and brain activity, highlighting the potential applications in therapeutic settings, cognitive training, and educational tools. The findings are especially relevant for developing VR gaming content and therapeutic games, enhancing the understanding of cognitive processes, and aiding in mental healthcare strategies.
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(This article belongs to the Special Issue Serious Games and Extended Reality (XR))
Open AccessArticle
Research on 3D Visualization of Drone Scenes Based on Neural Radiance Fields
by
Pengfei Jin and Zhuoyuan Yu
Electronics 2024, 13(9), 1682; https://doi.org/10.3390/electronics13091682 - 26 Apr 2024
Abstract
Neural Radiance Fields (NeRFs), as an innovative method employing neural networks for the implicit representation of 3D scenes, have been able to synthesize images from arbitrary viewpoints and successfully apply them to the visualization of objects and room-level scenes (<50 m2).
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Neural Radiance Fields (NeRFs), as an innovative method employing neural networks for the implicit representation of 3D scenes, have been able to synthesize images from arbitrary viewpoints and successfully apply them to the visualization of objects and room-level scenes (<50 m2). However, due to the capacity limitations of neural networks, the rendering of drone-captured scenes (>10,000 m2) often appears blurry and lacks detail. Merely increasing the model’s capacity or the number of sample points can significantly raise training costs. Existing space contraction methods, designed for forward-facing trajectory or the 360° object-centric trajectory, are not suitable for the unique trajectories of drone footage. Furthermore, anomalies and cloud fog artifacts, resulting from complex lighting conditions and sparse data acquisition, can significantly degrade the quality of rendering. To address these challenges, we propose a framework specifically designed for drone-captured scenes. Within this framework, while using a feature grid and multi-layer perceptron (MLP) to jointly represent 3D scenes, we introduce a Space Boundary Compression method and a Ground-Optimized Sampling strategy to streamline spatial structure and enhance sampling performance. Moreover, we propose an anti-aliasing neural rendering model based on Cluster Sampling and Integrated Hash Encoding to optimize distant details and incorporate an L1 norm penalty for outliers, as well as entropy regularization loss to reduce fluffy artifacts. To verify the effectiveness of the algorithm, experiments were conducted on four drone-captured scenes. The results show that, with only a single GPU and less than two hours of training time, photorealistic visualization can be achieved, significantly improving upon the performance of the existing NeRF approaches.
Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)
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