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
Attitude Calculation Method of Drilling Tools Based on Cross-Correlation Extraction and ASRUKF
Electronics 2024, 13(9), 1707; https://doi.org/10.3390/electronics13091707 (registering DOI) - 28 Apr 2024
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As a key component of the measurement while drilling technology, the accuracy of attitude calculation is directly related to the efficiency of resource exploration. To reduce the influence of vibration, rotation, and other disturbances on the attitude sensor during drilling, a method based
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As a key component of the measurement while drilling technology, the accuracy of attitude calculation is directly related to the efficiency of resource exploration. To reduce the influence of vibration, rotation, and other disturbances on the attitude sensor during drilling, a method based on cross-correlation extraction and the adaptive square-root unscented Kalman filter (ASRUKF) is proposed to solve the attitude of the drilling tool in this paper. Firstly, the error of the signal collected by the attitude sensor is compensated, and the unscented Kalman filter (UKF) is used for filtering. Then, the effective gravitational acceleration signal is extracted by the cross-correlation method. Finally, an experimental platform for simulating the fully rotating attitude measurement system is established, and the application effects of the UKF and ASRUKF in the attitude calculation are compared. Compared with the UKF, the root mean square error of the inclination angle calculated by the ASRUKF is reduced by 12.9%, and the variance is reduced by 27.3%; the root mean square error of the azimuth angle is reduced by 29.5%, and the variance is reduced by 39.9%. The experimental results show that the attitude calculation method proposed in this paper can stably and effectively improve the accuracy of the attitude calculation of drilling tools.
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Open AccessArticle
A Benchmark for UAV-View Natural Language-Guided Tracking
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Hengyou Li, Xinyan Liu and Guorong Li
Electronics 2024, 13(9), 1706; https://doi.org/10.3390/electronics13091706 (registering DOI) - 28 Apr 2024
Abstract
We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and
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We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and natural language are carefully annotated. Compared to the existing data sets, which are only annotated with bounding boxes, the natural language sentences in our data set can be more suitable for many application fields where humans take part in the system for that language, being not only more friendly for human–computer interaction but also capable of overcoming the appearance features’ low uniqueness for tracking. We tested several existing methods on our new benchmarks and found that the performance of the existing methods was not satisfactory. To pave the way for future work, we propose a baseline method suitable for this task, achieving state-of-the-art performance. We believe our new data set and proposed baseline method will be helpful in many fields, such as smart city, smart transportation, vehicle management, etc.
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(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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Open AccessArticle
Design and Test of Offset Quadrature Phase-Shift Keying Modulator with GF180MCU Open Source Process Design Kit
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Emma Mascorro-Guardado, Susana Ortega-Cisneros, Emilio Isaac Baungarten-Leon, Luis A. Luna-Rodriguez, Uriel Jaramillo-Toral, Manuel Hernández-Aramburo and Emanuel Murillo-García
Electronics 2024, 13(9), 1705; https://doi.org/10.3390/electronics13091705 (registering DOI) - 28 Apr 2024
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This article explores the evolution of integrated circuits , highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and
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This article explores the evolution of integrated circuits , highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and details the process of implementing the digital stage in offset quadrature phase-shift keying (OQPSK) modulation in an IC, including its hardware description language (HDL), the implementation test in the field-programmable gate array (FPGA), and the physical layout using the first manufactured open source process design kits (PDKs) in Global Foundries’ 180 nm, as well as the use of OpenLane and Caravel. To conclude, the results of the physical tests obtained from the digital modulation are presented, as well as the performance of the raised cosine shaping filter.
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Open AccessArticle
The Genesis of AIbyAI Integrated Circuit: Where AI Creates AI
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Emilio Isaac Baungarten-Leon, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales and German Pinedo-Diaz
Electronics 2024, 13(9), 1704; https://doi.org/10.3390/electronics13091704 (registering DOI) - 28 Apr 2024
Abstract
The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis
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The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AIbyAI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.
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(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
Open AccessArticle
Modulation of Diamond PN Junction Diode with Double-Layered n-Type Diamond by Using TCAD Simulation
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Caoyuan Mu, Genzhuang Li, Xianyi Lv, Qiliang Wang, Hongdong Li, Liuan Li and Guangtian Zou
Electronics 2024, 13(9), 1703; https://doi.org/10.3390/electronics13091703 (registering DOI) - 28 Apr 2024
Abstract
This study proposed a novel double-layer junction termination structure for vertical diamond-based PN junction diodes (PND). The effects of the geometry and doping concentration of the junction termination structure on the PNDs’ electrical properties are investigated using Silvaco TCAD software (Version 5.0.10.R). It
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This study proposed a novel double-layer junction termination structure for vertical diamond-based PN junction diodes (PND). The effects of the geometry and doping concentration of the junction termination structure on the PNDs’ electrical properties are investigated using Silvaco TCAD software (Version 5.0.10.R). It demonstrates that the electric performances of PND with a single n-type diamond layer are sensitive to the doping concentration and electrode location of the n-type diamond. To further suppress the electric field crowding and obtain a better balance between breakdown voltage and on-resistance, a double-layer junction termination structure is introduced and evaluated, yielding significantly improved electronic performances. Those results provide some useful thoughts for the design of vertical diamond PND devices.
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(This article belongs to the Special Issue Recent Advances in Wide Bandgap Semiconductors)
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Open AccessArticle
SiC Fin-Channel MOSFET for Enhanced Gate Shielding Effect
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Ling Sang, Rui Jin, Jiawei Cui, Xiping Niu, Zheyang Li, Junjie Yang, Muqin Nuo, Meng Zhang, Maojun Wang and Jin Wei
Electronics 2024, 13(9), 1701; https://doi.org/10.3390/electronics13091701 (registering DOI) - 28 Apr 2024
Abstract
A SiC fin-channel MOSFET structure (Fin-MOS) is proposed for an enhanced gate shielding effect. The gates are placed on each side of the narrow fin-channel region, while grounded p-shield regions below the gates provide a strong shielding effect. The device is investigated using
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A SiC fin-channel MOSFET structure (Fin-MOS) is proposed for an enhanced gate shielding effect. The gates are placed on each side of the narrow fin-channel region, while grounded p-shield regions below the gates provide a strong shielding effect. The device is investigated using Sentaurus TCAD. For a narrow fin-channel region, there is difficulty in forming an Ohmic contact to the p-base; a floating p-base might potentially store negative charges upon high drain voltage, and, thus, causes threshold voltage instabilities. The simulation reveals that, for a fin-width of 0.2 μm, the p-shield regions provide a stringent shielding effect against high drain voltage, and the dynamic threshold voltage shift (∆Vth) is negligible. Compared to conventional trench MOSFET (Trench-MOS) and asymmetric trench MOSFET (Asym-MOS), the proposed Fin-MOS boasts the lowest OFF-state oxide field and reverse transfer capacitance (Crss), while maintaining a similar low ON-resistance.
Full article
(This article belongs to the Special Issue Wide Bandgap Semiconductor: From Epilayer to Devices)
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Open AccessArticle
DiT-Gesture: A Speech-Only Approach to Stylized Gesture Generation
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Fan Zhang, Zhaohan Wang, Xin Lyu, Naye Ji, Siyuan Zhao and Fuxing Gao
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
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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.
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(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
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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.
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(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
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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.
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(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
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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.
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(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
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Zhongjie Jiang, Zhiqiang Li and Zhenjie Yao
Electronics 2024, 13(9), 1696; https://doi.org/10.3390/electronics13091696 (registering DOI) - 27 Apr 2024
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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
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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
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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
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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
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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)
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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.
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(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.
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(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)
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