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Volume 13
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Electronics, Volume 13, Issue 18 (September-2 2024) – 18 articles

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23 pages, 803 KiB  
Article
Blockchain-Based Spectrum Sharing Algorithm for UAV-Assisted Relay System
by Fukang Huang and Qi Zhu
Electronics 2024, 13(18), 3600; https://doi.org/10.3390/electronics13183600 - 10 Sep 2024
Abstract
Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted [...] Read more.
Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir’s secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions. Full article
20 pages, 872 KiB  
Article
Multi-Objective Parameter Configuration Optimization of Hydrogen Fuel Cell Hybrid Power System for Locomotives
by Suyao Liu, Chunmei Xu, Yifei Zhang, Haoying Pei, Kan Dong, Ning Yang and Yingtao Ma
Electronics 2024, 13(18), 3599; https://doi.org/10.3390/electronics13183599 - 10 Sep 2024
Abstract
Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these [...] Read more.
Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS. Full article
(This article belongs to the Special Issue Recent Advances in Electrified Vehicles and Transportation Electrification)
12 pages, 5383 KiB  
Article
A Fully Synthesizable Fractional-N Digital Phase-Locked Loop with a Calibrated Dual-Referenced Interpolating Time-to-Digital Converter to Compensate for Process–Voltage–Temperature Variations
by Seojin Kim, Youngsik Kim, Hyunwoo Son and Shinwoong Kim
Electronics 2024, 13(18), 3598; https://doi.org/10.3390/electronics13183598 - 10 Sep 2024
Abstract
This paper presents advancements in the performance of digital phase-locked loop (DPLL)s, with a special focus on addressing the issue of required gain calibration in the time-to-digital converter (TDC) within phase-domain DPLL structures. Phase-domain DPLLs are preferred for their simplicity in implementation and [...] Read more.
This paper presents advancements in the performance of digital phase-locked loop (DPLL)s, with a special focus on addressing the issue of required gain calibration in the time-to-digital converter (TDC) within phase-domain DPLL structures. Phase-domain DPLLs are preferred for their simplicity in implementation and for eliminating the delta–sigma modulator (DSM) noise inherent in conventional fractional-N designs. However, this advantage is countered by the critical need to calibrate the gain of the TDC. The previously proposed dual-interpolated TDC(DI-TDC) was proposed as a solution to this problem, but strong spurs were still generated due to the TDC resolution, which easily became non-uniform due to PVT variation, degrading performance. To overcome these problems, this work proposes a DPLL with a new calibration system that ensures consistent TDC resolution matching the period of the digitally controlled oscillator (DCO) and operating in both the foreground and background, thereby maintaining consistent performance despite PVT variations. This study proposes a DPLL using a calibrated dual-interpolated TDC that effectively compensates for PVT variations and improves the stability and performance of the DPLL. The PLL was fabricated in a 28-nm CMOS process with an active area of only 0.019 mm2, achieving an integrated phase noise (IPN) performance of −17.5 dBc, integrated from 10 kHz to 10 MHz at a PLL output of 570 MHz and −20.5 dBc at 1.1 GHz. This PLL operates within an output frequency range of 475 MHz to 1.1 GHz. Under typical operating conditions, it consumes only 930 µW with a 1.0 V supply. Full article
(This article belongs to the Special Issue Advances in Low Powered Circuits Design and Their Application)
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16 pages, 5923 KiB  
Article
Navigating in Light: Precise Indoor Positioning Using Trilateration and Angular Diversity in a Semi-Spherical Photodiode Array with Visible Light Communication
by Javier Barco Alvárez, Juan Carlos Torres Zafra, Juan Sebastián Betancourt, Máximo Morales Cespedes and Carlos Iván del Valle Morales
Electronics 2024, 13(18), 3597; https://doi.org/10.3390/electronics13183597 - 10 Sep 2024
Abstract
This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of [...] Read more.
This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver’s position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system’s potential for significant indoor positioning accuracy and reliability improvements. Full article
(This article belongs to the Special Issue Precision Positioning and Navigation Communication Systems)
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16 pages, 10064 KiB  
Article
A Wireless Ad Hoc Network Communication Platform and Data Transmission Strategies for Multi-Bus Instruments
by Lushuai Qian, Kexin Gu, Yaqiong Fu, Yuli Shen and Suan Xu
Electronics 2024, 13(18), 3596; https://doi.org/10.3390/electronics13183596 - 10 Sep 2024
Abstract
As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated [...] Read more.
As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated configuration, issues with multi-system collaboration, and data blocking. This paper proposes a wireless ad hoc network platform for multi-bus instruments based on a low-cost ESP-12H WiFi module. The platform supports GPIB, RS232, RS485, and CAN bus interface instrument access. It features easy configuration, ad hoc networking, and self-repairing capabilities. A relay multi-hop network with a tree topology expands capacity and coverage. Additionally, a dynamic window-receiving mode and an improved multi-priority queue ensure data transmission integrity. The experimental results show that the platform’s networking time is less than 10 s, and the coverage range reaches 50 m in complex indoor environments. It also shows good stability when running for a long time. However, due to hardware and software design limitations, the actual upload speeds fall short of the theoretical values. For example, RS232 and RS485 are about 10% slower than the theoretical values, and GPIB is about 80% slower. Further optimization is required in the future. Full article
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13 pages, 5820 KiB  
Article
Optic Nerve Sheath Ultrasound Image Segmentation Based on CBC-YOLOv5s
by Yonghua Chu, Jinyang Xu, Chunshuang Wu, Jianping Ye, Jucheng Zhang, Lei Shen, Huaxia Wang and Yudong Yao
Electronics 2024, 13(18), 3595; https://doi.org/10.3390/electronics13183595 - 10 Sep 2024
Abstract
The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive methods. Compared to the invasive methods, the [...] Read more.
The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive methods. Compared to the invasive methods, the non-invasive methods are safer and have thus gained popularity. Among the non-invasive methods, using deep learning to process the ultrasound images of the eyes of critically ill patients and promptly output the diameter of the optic nerve sheath offers significant advantages. This paper proposes a CBC-YOLOv5s optic nerve sheath ultrasound image segmentation method that integrates both local and global features. First, it introduces the CBC-Backbone feature extraction network, which consists of dual-layer C3 Swin-Transformer (C3STR) and dual-layer Bottleneck Transformer (BoT3) modules. The C3STR backbone’s multi-layer convolution and residual connections focus on the local features of the optic nerve sheath, while the Window Transformer Attention (WTA) mechanism in the C3STR module and the Multi-Head Self-Attention (MHSA) in the BoT3 module enhance the model’s understanding of the global features of the optic nerve sheath. The extracted local and global features are fully integrated in the Spatial Pyramid Pooling Fusion (SPPF) module. Additionally, the CBC-Neck feature pyramid is proposed, which includes a single-layer C3STR module and three-layer CReToNeXt (CRTN) module. During upsampling feature fusion, the C3STR module is used to enhance the local and global awareness of the fused features. During downsampling feature fusion, the CRTN module’s multi-level residual design helps the network to better capture the global features of the optic nerve sheath within the fused features. The introduction of these modules achieves the thorough integration of the local and global features, enabling the model to efficiently and accurately identify the optic nerve sheath boundaries, even when the ocular ultrasound images are blurry or the boundaries are unclear. The Z2HOSPITAL-5000 dataset collected from Zhejiang University Second Hospital was used for the experiments. Compared to the widely used YOLOv5s and U-Net algorithms, the proposed method shows improved performance on the blurry test set. Specifically, the proposed method achieves precision, recall, and Intersection over Union (IoU) values that are 4.1%, 2.1%, and 4.5% higher than those of YOLOv5s. When compared to U-Net, the precision, recall, and IoU are improved by 9.2%, 21%, and 19.7%, respectively. Full article
(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)
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16 pages, 978 KiB  
Article
Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems
by Hongchan Li, Jinming Zheng, Baohua Jin and Haodong Zhu
Electronics 2024, 13(18), 3594; https://doi.org/10.3390/electronics13183594 - 10 Sep 2024
Abstract
Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation [...] Read more.
Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively. Full article
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19 pages, 1271 KiB  
Article
A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance
by Deyang Yin, Yuanyuan Zhu, Hao Qiang, Jianfeng Zheng and Zhenzhong Zhang
Electronics 2024, 13(18), 3593; https://doi.org/10.3390/electronics13183593 - 10 Sep 2024
Abstract
A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. [...] Read more.
A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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28 pages, 7506 KiB  
Article
Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System
by Xinghua Wu, Mingzhe Wang, Jinsheng Shen and Yanwei Gong
Electronics 2024, 13(18), 3592; https://doi.org/10.3390/electronics13183592 - 10 Sep 2024
Abstract
Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR [...] Read more.
Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general. Full article
(This article belongs to the Special Issue Reliability, Fault Tolerance and Safety of Electronic Devices and Systems)
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22 pages, 8264 KiB  
Article
Ray-Tracing-Assisted SAR Image Simulation under Range Doppler Imaging Geometry
by Junjie Li, Gaohao Zhu, Chen Hou, Wenya Zhang, Kang Du, Chuanxiang Cheng and Ke Wu
Electronics 2024, 13(18), 3591; https://doi.org/10.3390/electronics13183591 - 10 Sep 2024
Abstract
In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic [...] Read more.
In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic (EM) waves into the SAR radiation area and follows the Nyquist sampling law to set the density of transmitted EM waves to effectively identify the beam radiation area. The ray-tracing algorithm is used to obtain the backscatter amplitude and real-time slant range of the transmitted EM wave, which can effectively record the multiple backscattering among the components of the distributed target so that the backscattering subfields of each component can be correlated. According to the RD condition equation, the backscattering amplitude is assigned to the corresponding range gate, and the three-dimensional (3D) target is mapped into the two-dimensional (2D) SAR slant-range coordinate system, and the SAR target simulated image is directly obtained. Finally, the simulation images of the proposed method are compared qualitatively and quantitatively with those obtained by commercial simulation software, and the effectiveness of the proposed method is verified. Full article
(This article belongs to the Special Issue SAR Image and Signal Processing)
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23 pages, 2029 KiB  
Article
Task Offloading and Resource Allocation for Augmented Reality Applications in UAV-Based Networks Using a Dual Network Architecture
by Dat Van Anh Duong, Shathee Akter and Seokhoon Yoon
Electronics 2024, 13(18), 3590; https://doi.org/10.3390/electronics13183590 - 10 Sep 2024
Abstract
This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource [...] Read more.
This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource allocation for AR tasks with dependent relationships. This work specifically focuses on the problem of dependent tasks in AR applications within UAV-based networks. This problem has not been thoroughly addressed in previous research. A dual network architecture-based task offloading (DNA-TO) algorithm is proposed, leveraging the DNA framework to enhance decision-making in reinforcement learning while mitigating noise. In addition, a Karush–Kuhn–Tucker-based resource allocation (KKT-RA) algorithm is proposed to optimize resource allocation. Various simulations using real-world movement data are conducted. The results indicate that our proposed algorithm outperforms existing approaches in terms of latency and energy efficiency. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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22 pages, 30010 KiB  
Article
AmazingFT: A Transformer and GAN-Based Framework for Realistic Face Swapping
by Li Liu, Dingli Tong, Wenhua Shao and Zhiqiang Zeng
Electronics 2024, 13(18), 3589; https://doi.org/10.3390/electronics13183589 - 10 Sep 2024
Abstract
Current face-swapping methods often suffer from issues of detail blurriness and artifacts in generating high-quality images due to the inherent complexity in detail processing and feature mapping. To overcome these challenges, this paper introduces the Amazing Face Transformer (AmazingFT), an advanced face-swapping model [...] Read more.
Current face-swapping methods often suffer from issues of detail blurriness and artifacts in generating high-quality images due to the inherent complexity in detail processing and feature mapping. To overcome these challenges, this paper introduces the Amazing Face Transformer (AmazingFT), an advanced face-swapping model built upon Generative Adversarial Networks (GANs) and Transformers. The model is composed of three key modules: the Face Parsing Module, which segments facial regions and generates semantic masks; the Amazing Face Feature Transformation Module (ATM), which leverages Transformers to extract and transform features from both source and target faces; and the Amazing Face Generation Module (AGM), which utilizes GANs to produce high-quality swapped face images. Experimental results demonstrate that AmazingFT outperforms existing state-of-the-art (SOTA) methods, significantly enhancing detail fidelity and occlusion handling, ultimately achieving movie-grade face-swapping results. Full article
(This article belongs to the Section Artificial Intelligence)
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24 pages, 4921 KiB  
Article
DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure
by Kai Jia, Jun Xiang and Baoxia Li
Electronics 2024, 13(18), 3588; https://doi.org/10.3390/electronics13183588 - 10 Sep 2024
Abstract
Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and [...] Read more.
Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM. Full article
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17 pages, 1076 KiB  
Article
Prompt-Based End-to-End Cross-Domain Dialogue State Tracking
by Hengtong Lu, Lucen Zhong, Huixing Jiang, Wei Chen, Caixia Yuan and Xiaojie Wang
Electronics 2024, 13(18), 3587; https://doi.org/10.3390/electronics13183587 - 10 Sep 2024
Abstract
Cross-domain dialogue state tracking (DST) focuses on using labeled data from source domains to train a DST model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing cross-domain DST models track each [...] Read more.
Cross-domain dialogue state tracking (DST) focuses on using labeled data from source domains to train a DST model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing cross-domain DST models track each slot independently, which leads to poor performances caused by not considering the correlation among different slots, as well as low efficiency of training and inference. This paper, therefore, proposes a prompt-based end-to-end cross-domain DST method for efficiently tracking all slots simultaneously. A dynamic prompt template shuffle method is proposed to alleviate the bias of the slot order, and a dynamic prompt template sampling method is proposed to alleviate the bias of the slot number, respectively. The experimental results on the MultiWOZ 2.0 and MultiWOZ 2.1 datasets show that our approach consistently outperforms the state-of-the-art baselines in all target domains and improves both training and inference efficiency by at least 5 times. Full article
(This article belongs to the Special Issue Data Mining Applied in Natural Language Processing)
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13 pages, 7416 KiB  
Article
Novel Power-Efficient Fast-Locking Phase-Locked Loop Based on Adaptive Time-to-Digital Converter-Aided Acceleration Compensation Technology
by Ligong Sun, Yixin Luo, Zhiyao Deng, Jinchan Wang and Bo Liu
Electronics 2024, 13(18), 3586; https://doi.org/10.3390/electronics13183586 - 10 Sep 2024
Abstract
This paper proposes an adaptive acceleration lock compensation technology for phase-locked loops (PLLs) based on a novel dual-mode programmable ring voltage-controlled oscillator (ring-VCO). In addition, a time-to-digital converter (TDC) is designed to accurately quantify the phase difference from the phase frequency detector (PFD) [...] Read more.
This paper proposes an adaptive acceleration lock compensation technology for phase-locked loops (PLLs) based on a novel dual-mode programmable ring voltage-controlled oscillator (ring-VCO). In addition, a time-to-digital converter (TDC) is designed to accurately quantify the phase difference from the phase frequency detector (PFD) in order to optimize the dead-zone effect while dynamically switching an auxiliary charge pump (CP) module to realize fast phase locking. Furthermore, a TDC-controlled three/five-stage dual-mode adaptively continuously switched VCO is proposed to optimize the phase noise (PN) and power efficiency, leading to an optimal performance tradeoff of the PLL. Based on the 180 nm/1.8 V standard CMOS technology, the complete PLL design and a corresponding simulation analysis are implemented. The results show that, with a 1 GHz reference signal as the input, the output frequency is 50–324 MHz, with a wide tuning range of 260 MHz and a low phase noise of −98.07 dBc/Hz@1 MHz. The key phase-locking time is reduced to 1.11 μs, and the power dissipation is lowered to 1.86 mW with a layout area of 66 μm × 128 μm. A significantly remarkable multiobjective performance tradeoff with topology optimization is realized, which is in contrast to several similar design cases of PLLs. Full article
(This article belongs to the Section Circuit and Signal Processing)
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15 pages, 42374 KiB  
Article
Twelve-Element MIMO Wideband Antenna Array Operating at 3.3 GHz for 5G Smartphone Applications
by Hehe Yu, Xinwen Shang, Qianzhong Xue, Haibing Ding, Jing Wang, Weiwei Lv and Yuanzhe Luo
Electronics 2024, 13(18), 3585; https://doi.org/10.3390/electronics13183585 - 10 Sep 2024
Abstract
This work presents a 12-element multiple-input–multiple-output (MIMO) wideband antenna array for mobile smartphones. The antenna element is mainly composed of two parts, greatly improving the antenna array bandwidth: one is a meandering, looped radiating element and the other is a U-shaped slot. For [...] Read more.
This work presents a 12-element multiple-input–multiple-output (MIMO) wideband antenna array for mobile smartphones. The antenna element is mainly composed of two parts, greatly improving the antenna array bandwidth: one is a meandering, looped radiating element and the other is a U-shaped slot. For the antenna element design, the meandering, looped radiating element measures 12.95 × 6 mm2, while the U-shaped slot has a size of 15 × 3 mm2. Meanwhile, the reflection coefficient indicates that the designed antenna array operates at 3.3 GHz with a bandwidth of 500 MHz; the transmission coefficient shows that the isolation between the antenna elements is better than 12 dB. In addition, more antenna array performances are presented, including nearly omnidirectional radiation characteristics, antenna efficiency ranging from approximately 17 to 60%, envelope correlation coefficients (ECCs) below 0.065, and diversity gain (DG) values of the MIMO antenna system close to 10 dB. The measurement results are highly consistent with the simulation results of the designed wideband antenna array, indicating its great potential for future practical engineering applications. Full article
(This article belongs to the Special Issue Antenna Design and Its Applications)
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14 pages, 1620 KiB  
Article
Interpretable Support Vector Machine and Its Application to Rehabilitation Assessment
by Woojin Kim, Hyunwoo Joe, Hyun-Suk Kim and Daesub Yoon
Electronics 2024, 13(18), 3584; https://doi.org/10.3390/electronics13183584 - 10 Sep 2024
Abstract
This paper does present an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become [...] Read more.
This paper does present an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become those from normal cases. This analytical approach is computationally simple and provides a unique solution. The nearest boundary point of abnormal data can also be used to analyze the cause of abnormal classification and indicate countermeasures for normalization. These properties render the proposed interpretable SVM valuable for medical assessment applications and other problems that require careful consideration of classification results for treatment. Simulation and application results demonstrate the feasibility and effectiveness of the proposed method. Full article
(This article belongs to the Section Bioelectronics)
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19 pages, 1565 KiB  
Article
Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks
by Kai Xu, Zemin Li, Nan Liang, Fanchun Kong, Shaobo Lei, Shengjie Wang, Agyemang Paul and Zhefu Wu
Electronics 2024, 13(18), 3583; https://doi.org/10.3390/electronics13183583 - 10 Sep 2024
Abstract
With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS [...] Read more.
With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks. Full article
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