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
Decentralized Virtual Impedance Control for Power Sharing and Voltage Regulation in Islanded Mode with Minimized Circulating Current
Electronics 2024, 13(11), 2142; https://doi.org/10.3390/electronics13112142 (registering DOI) - 30 May 2024
Abstract
In islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well
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In islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well as the reverse circulating current between inverters, are problems in existing control strategies for parallel-connected inverters if mismatched line impedances are not addressed. Therefore, this study aims to develop an improved decentralized controller for good power sharing with voltage compensation using the predictive control scheme and circulating current minimization between the inverters’ current flow. The controller was developed based on adaptive virtual impedance (AVI) control, combined with finite control set–model predictive control (FCS-MPC). The AVI was used for the generation of reference voltage, which responded to the parameters from the virtual impedance loop control to be the input to the FCS-MPC for a faster tracking response and to have minimum tracking error for better pulse-width modulation generation in the space-vector form. As a result, the circulating current was maintained at below 5% and the inverters were able to share an equal power based on the load required. At the end, the performance of the AVI-based control scheme was compared with those of the conventional and static-virtual-impedance-based methods, which have also been tested in simulation using MATLAB/Simulink software 2021a version. The comparison results show that the AVI FCS MPC give 5% error compared to SVI at 10% and conventional PI at 20%, in which AVI is able to minimize the circulating current when mismatch impedance is applied to the DGs.
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(This article belongs to the Special Issue Advancements in Power Electronics Conversion Technologies)
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Open AccessArticle
Automatic Modulation Recognition Method Based on Phase Transformation and Deep Residual Shrinkage Network
by
Hao Chen, Wenpu Guo, Kai Kang and Guojie Hu
Electronics 2024, 13(11), 2141; https://doi.org/10.3390/electronics13112141 (registering DOI) - 30 May 2024
Abstract
Automatic Modulation Recognition (AMR) is currently a research hotspot, and research under low Signal-to-Noise Ratio (SNR) conditions still poses certain challenges. This paper proposes an AMR method based on phase transformation and deep residual shrinkage network to improve recognition accuracy. Firstly, the raw
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Automatic Modulation Recognition (AMR) is currently a research hotspot, and research under low Signal-to-Noise Ratio (SNR) conditions still poses certain challenges. This paper proposes an AMR method based on phase transformation and deep residual shrinkage network to improve recognition accuracy. Firstly, the raw I/Q data from the benchmark dataset RML2016.10a are used as the input. Then, an end-to-end modulation recognition is performed using the model. Phase transformation is used to correct the raw I/Q data and reduce the interference of phase shift on modulation recognition. Convolutional neural network (CNN) and Gate Recurrent Unit (GRU) extract the spatial and temporal features of the modulation signal, respectively. The improved deep residual shrinkage network is added after CNN to eliminate unimportant features through soft thresholding. Finally, the proposed model is trained and tested. The experimental results show that the proposed model notably reduces the number of parameters compared to other models, effectively improving the recognition accuracy under low SNR conditions. The average recognition accuracy reaches 62.46%, and the highest recognition accuracy reaches 92.41%.
Full article
Open AccessArticle
Training Acceleration Method Based on Parameter Freezing
by
Hongwei Tang, Jialiang Chen, Wenkai Zhang and Zhi Guo
Electronics 2024, 13(11), 2140; https://doi.org/10.3390/electronics13112140 (registering DOI) - 30 May 2024
Abstract
As deep learning has evolved, larger and deeper neural networks are currently a popular trend in both natural language processing tasks and computer vision tasks. With the increasing parameter size and model complexity in deep neural networks, it is also necessary to have
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As deep learning has evolved, larger and deeper neural networks are currently a popular trend in both natural language processing tasks and computer vision tasks. With the increasing parameter size and model complexity in deep neural networks, it is also necessary to have more data available for training to avoid overfitting and to achieve better results. These facts demonstrate that training deep neural networks takes more and more time. In this paper, we propose a training acceleration method based on gradually freezing the parameters during the training process. Specifically, by observing the convergence trend during the training of deep neural networks, we freeze part of the parameters so that they are no longer involved in subsequent training and reduce the time cost of training. Furthermore, an adaptive freezing algorithm for the control of freezing speed is proposed in accordance with the information reflected by the gradient of the parameters. Concretely, a larger gradient indicates that the loss function changes more drastically at that position, implying that there is more room for improvement with the parameter involved; a smaller gradient indicates that the loss function changes less and the learning of that part is close to saturation, with less benefit from further training. We use ViTDet as our baseline and conduct experiments on three remote sensing target detection datasets to verify the effectiveness of the method. Our method provides a minimum speedup ratio of 1.38×, while maintaining a maximum accuracy loss of only 2.5%.
Full article
(This article belongs to the Collection Deep Learning for Computer Vision: Algorithms, Theory and Application)
Open AccessCommunication
A Bayesian Deep Unfolded Network for the Off-Grid Direction-of-Arrival Estimation via a Minimum Hole Array
by
Ninghui Li, Xiaokuan Zhang, Fan Lv, Binfeng Zong and Weike Feng
Electronics 2024, 13(11), 2139; https://doi.org/10.3390/electronics13112139 (registering DOI) - 30 May 2024
Abstract
As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities
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As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities and lacks reasonable interpretability. Recently, a deep unfolding technique has attracted widespread concern due to the more explainable perspective and weaker data dependency. More importantly, it has been proven that deep unfolding enables convergence acceleration when applied to iterative algorithms. On this basis, we rigorously deduce an iterative sparse Bayesian learning (SBL) algorithm and construct a Bayesian deep unfolded network in a one-to-one correspondence. Moreover, the common but intractable off-grid errors, caused by grid mismatch, are directly considered in the signal model and computed in the iterative process. In addition, minimum hole array, little considered in deep unfolding, is adopted to further improve estimation performance owing to the maximized array degrees of freedom (DOFs). Extensive simulation results are presented to illustrate the superiority of the proposed method beyond other state-of-the-art methods.
Full article
(This article belongs to the Section Microwave and Wireless Communications)
Open AccessArticle
Neural Chaotic Oscillation: Memristive Feedback, Symmetrization, and Its Application in Image Encryption
by
Keyu Huang, Chunbiao Li, Yongxin Li, Tengfei Lei and Haiyan Fu
Electronics 2024, 13(11), 2138; https://doi.org/10.3390/electronics13112138 (registering DOI) - 30 May 2024
Abstract
The symmetry of neuron discharging has some relationship with the electrophysiological characteristics and dynamic behavior of a neuron, and has a close relation with the symmetry of ion channels, current balance, neuron type, synaptic transmission, and network effects. Among them, the feedback and
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The symmetry of neuron discharging has some relationship with the electrophysiological characteristics and dynamic behavior of a neuron, and has a close relation with the symmetry of ion channels, current balance, neuron type, synaptic transmission, and network effects. Among them, the feedback and interactions in the network have a particularly direct impact on the symmetrical discharge of a neuron element. This work introduces a memristor as a synapse into a neuron cell, taking the membrane potential back to ion channels, and therefore various symmetric firing behaviors of Hindmarsh–Rose (HR) neurons are observed, including chaos and various periodic firings. By further adjusting the feedback, coexisting symmetrical discharge of the neuron is achieved. Furthermore, the impact of frequency variations on the memristor synapse is analyzed, and thus the operating regimes of memristor and resistor are classified and discussed. Circuit simulations prove the neural chaotic firings along with their symmetrized discharging processes, demonstrating the effectiveness of symmetrical control of chaotic discharge. Finally, applying the symmetrical system to DNA image encryption can effectively protect the security of images.
Full article
(This article belongs to the Special Issue Recent Advances in Chaotic Systems and Their Security Applications, 2nd edition)
Open AccessArticle
Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images
by
Yu Zeng, Kun Wang, Lai Dai, Changqing Wang, Chi Xiong, Peng Xiao, Bin Cai, Qiang Zhang, Zhiyong Sun, Erkang Cheng and Bo Song
Electronics 2024, 13(11), 2137; https://doi.org/10.3390/electronics13112137 (registering DOI) - 30 May 2024
Abstract
Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach
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Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively.
Full article
(This article belongs to the Special Issue Neural Networks for Feature Extraction)
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Open AccessArticle
Efficient Gaussian Process Calculations Using Chebyshev Nodes and Fast Fourier Transform
by
Adrian Dudek and Jerzy Baranowski
Electronics 2024, 13(11), 2136; https://doi.org/10.3390/electronics13112136 - 30 May 2024
Abstract
Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However,
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Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However, a common problem in the application of Gaussian processes is their computational complexity. To address this challenge, sparse methods are frequently employed, involving a reduction in the computational domain. In this study, we propose an innovative computational approach for Gaussian processes. Our method revolves around selecting a computation domain based on Chebyshev nodes, with the optimal number of nodes determined by minimizing the degree of the Chebyshev series, while ensuring meaningful coefficients derived from function values at the Chebyshev nodes with fast Fourier transform. This approach not only facilitates a reduction in computation time but also provides a means to reconstruct the original function using the functional series. We conducted experiments using two computational methods for Gaussian processes: Markov chain Monte Carlo and integrated nested Laplace approximation. The results demonstrate a significant reduction in computation time, thereby motivating further development of the proposed algorithm.
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(This article belongs to the Section Systems & Control Engineering)
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Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing
by
Xiaobao Sha, Wenjian Sun, Xiang Liu, Yang Luo and Chunbo Luo
Electronics 2024, 13(11), 2135; https://doi.org/10.3390/electronics13112135 - 30 May 2024
Abstract
Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered
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Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client–edge model aggregation with synchronous edge–cloud model aggregation. By leveraging the clients’ idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients’ local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures.
Full article
(This article belongs to the Special Issue Recent Advances in Collaborative Systems and Control in the Industrial Sector)
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Combined Use of Python and DIgSILENT PowerFactory to Analyse Power Systems with Large Amounts of Variable Renewable Generation
by
Javier Jiménez-Ruiz, Andrés Honrubia-Escribano and Emilio Gómez-Lázaro
Electronics 2024, 13(11), 2134; https://doi.org/10.3390/electronics13112134 - 30 May 2024
Abstract
Over the last decade considerable efforts have been made to reduce greenhouse gas emissions, leading to the significant development and implementation of renewable energy plants across all power systems in the world. Wind energy has consolidated its position as one of the two
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Over the last decade considerable efforts have been made to reduce greenhouse gas emissions, leading to the significant development and implementation of renewable energy plants across all power systems in the world. Wind energy has consolidated its position as one of the two key energy sources (in conjunction with solar photovoltaics) to achieve completely green power systems. Integrating wind energy into power systems is a more complicated task compared to traditional generation systems, as wind energy relies on a variable energy source characterised by high variability. Several tools currently exist to simulate the effect of wind energy generation in power systems, but they often lack the versatility demanded by researchers. This paper analyses how both Python 3.11 and DIgSILENT PowerFactory 2024 can be used synergistically to assess the implementation of wind power plants, highlighting how the use of these two tools combined can be of great interest for both researchers and grid operators.
Full article
(This article belongs to the Special Issue Planning, Operation and Control of Power Systems with Large Amounts of Variable Renewable Generation)
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Open AccessArticle
Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions
by
Zhidan Zhong, Zhihui Zhang, Yunhao Cui, Xinghui Xie and Wenlu Hao
Electronics 2024, 13(11), 2133; https://doi.org/10.3390/electronics13112133 - 30 May 2024
Abstract
Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in
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Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios.
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(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
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Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity
by
Chin-Yuan Lin, Jen-Chun Lee, Shuenn-Jyi Wang, Chung-Shi Chiang and Chao-Lung Chou
Electronics 2024, 13(11), 2132; https://doi.org/10.3390/electronics13112132 - 30 May 2024
Abstract
With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research
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With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.
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(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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An Accurate Cooperative Localization Algorithm Based on RSS Model and Error Correction in Wireless Sensor Networks
by
Bo Chang, Xinrong Zhang and Haiyi Bian
Electronics 2024, 13(11), 2131; https://doi.org/10.3390/electronics13112131 - 30 May 2024
Abstract
Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of
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Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of positioning. At the ranging stage, the RSSI measurement value is converted to distance by wireless channel modeling and the dynamic acquisition of the power attenuation factor. Then, the ranging correction is carried out by using the known anchor node ranging error information. The Taylor series expansion least-square iterative refinement algorithm is implemented in the position optimization stage, and satisfactory positioning accuracy is obtained. The idea of cooperative positioning is introduced to upgrade the nodes that meet the requirements and are upgraded to anchor nodes and participate in the positioning of other nodes to improve the positioning coverage and positioning accuracy. The experimental results show that the localization effect of this algorithm is close to that of the Taylor series expansion algorithm based on coordinates but far higher than that of the basic least-squares localization algorithm. The positioning accuracy can be improved rapidly with the decrease in the distance measurement error.
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(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
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Developing Different Test Conditions to Verify the Robustness and Versatility of Robotic Arms Controlled by Evolutionary Algorithms
by
Roland Szabo
Electronics 2024, 13(11), 2130; https://doi.org/10.3390/electronics13112130 - 29 May 2024
Abstract
In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using
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In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using it to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm’s mechanical structure, electronics, and software are put to the test. The software is based on evolutionary software that implements genetic algorithms. The entire command system is also ported to FPGAs (to hardware) to increase speed and response time.
Full article
(This article belongs to the Section Industrial Electronics)
Open AccessArticle
Rapid Beam Tracking Using Power Measurement for Terahertz Communications
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Xiaodan He, Changming Zhang, Chi Lu and Xianbin Yu
Electronics 2024, 13(11), 2129; https://doi.org/10.3390/electronics13112129 - 29 May 2024
Abstract
With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow
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With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow beams are expected to be adopted, making beam tracking significant for robust communications. In this paper, a tracking method based on power measurement is proposed, consisting of beam status monitoring, recognition of the deviation direction, and movement toward the optimal angle. By observing the change in the received signal power, beam misalignment is first checked, and whether the misalignment is out of tracking range is also determined. Then, the deviation direction is recognized by comparing the received power variations in the candidate directions, and the beam angle is adjusted accordingly until it reaches the optimal angle. With a small scanning range, the deviation direction is recognized in a short duration, allowing for rapid beam tracking. Numerical results indicate that the alignment error is competitively low and stable in the proposed beam tracking method, and its technical superiority is particularly dominant in situations involving variable motion at high speeds.
Full article
(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)
Open AccessArticle
Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints
by
Spyridon Anogiatis, Panagiotis S. Trakas and Charalampos P. Bechlioulis
Electronics 2024, 13(11), 2128; https://doi.org/10.3390/electronics13112128 - 29 May 2024
Abstract
This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative
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This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.
Full article
(This article belongs to the Special Issue Path Planning for Mobile Robots, 2nd Edition)
Open AccessArticle
Parameter Optimization of Josephson Parametric Amplifiers Using a Heuristic Search Algorithm for Axion Haloscope Search
by
Younggeun Kim, Junu Jeong, Sungwoo Youn, Sungjae Bae, Arjan F. van Loo, Yasunobu Nakamura, Sergey Uchaikin and Yannis K. Semertzidis
Electronics 2024, 13(11), 2127; https://doi.org/10.3390/electronics13112127 - 29 May 2024
Abstract
The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known
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The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known for its quantum-limited noise characteristics, has been incorporated into the detection system to capture the weakly interacting axion signals. However, the performance of the JPA can be influenced by its environment, leading to the potential unreliability of a predefined parameter set obtained in a specific laboratory setting. Furthermore, conducting a broadband search requires the consecutive characterization of the amplifier across different tuning frequencies. To ensure more reliable measurements, we utilize the Nelder–Mead technique as a numerical search method to dynamically determine the optimal operating conditions. This heuristic search algorithm explores the multidimensional parameter space of the JPA, optimizing critical characteristics such as gain and noise temperature to maximize signal-to-noise ratios for a given experimental setup. Our study presents a comprehensive analysis of the properties of a flux-driven JPA to demonstrate the effectiveness of the algorithm. This approach contributes to ongoing efforts in axion dark matter research by offering an efficient method to enhance axion detection sensitivity through the optimized utilization of JPAs.
Full article
(This article belongs to the Special Issue Recent Advances and Applications in New Detectors)
Open AccessArticle
Vulnerability Assessment and Topology Reconstruction of Task Chains in UAV Networks
by
Qingfeng Yue, Jinglei Li, Zijia Huang, Xiaoyu Xie and Qinghai Yang
Electronics 2024, 13(11), 2126; https://doi.org/10.3390/electronics13112126 - 29 May 2024
Abstract
With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such
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With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage.
Full article
(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
Open AccessArticle
A High-Performance Non-Indexed Text Search System
by
Binh Kieu-Do-Nguyen, Tuan-Kiet Dang, Nguyen The Binh, Cuong Pham-Quoc, Huynh Phuc Nghi, Ngoc-Thinh Tran, Katsumi Inoue, Cong-Kha Pham and Trong-Thuc Hoang
Electronics 2024, 13(11), 2125; https://doi.org/10.3390/electronics13112125 - 29 May 2024
Abstract
Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful
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Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful the index algorithm, the more memory and processing time are required. The amount of time and memory required to index a collection of documents is proportional to its overall size. In this paper, we propose a full-text search hardware implementation without the indexing phase, thus removing the time and memory requirements for indexing. Additionally, we propose an efficient design to leverage the parallel architecture of High Bandwidth Memory (HBM). To our knowledge, few (if not zero) researchers have integrated their full-text search system with an effective data access control on HBM. The functionality of the proposed system is verified on the Xilinx Alveo U50 Field-Programmable Gate Array (FPGA). The experimental results show that our system achieved a throughput of 8 Gigabytes per second, about 6697× speed-up compared to other software-based approaches.
Full article
(This article belongs to the Section Microelectronics)
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Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship
by
Gangui Yan, Yupeng Wang, Yuxing Fan, Cheng Yang and Lin Yue
Electronics 2024, 13(11), 2124; https://doi.org/10.3390/electronics13112124 - 29 May 2024
Abstract
The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet
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The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet synchronous generators (PMSGs) connected to power systems poses a challenge when investigating small disturbance stability and instability mechanisms. This study establishes a model of the grid-connected PMSG system based on the voltage/power excitation–response relationship to describe the dynamic characteristics of the port of the PMSG, and the analysis of active and reactive response characteristics of PMSG lays the foundation for model simplification. Based on the unit model, a direct-drive wind farm aggregation equivalence method based on the excitation–response relationship is proposed. The equivalent model obtained by this method is suitable for the small disturbance stability analysis of direct-drive wind farms grid connected system, with good accuracy. The simulation verified the effectiveness of the aggregation model.
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(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
Open AccessArticle
Deep Pre-Training Transformers for Scientific Paper Representation
by
Jihong Wang, Zhiguang Yang and Zhanglin Cheng
Electronics 2024, 13(11), 2123; https://doi.org/10.3390/electronics13112123 - 29 May 2024
Abstract
In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain
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In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis.
Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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