Electronics
Electronics, an international, peer-reviewed Open Access journal.
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16 pages, 416 KiB

Open AccessArticle

A Dynamic Event-Based Recursive State Estimation for Mobile Robot Localization

by Li Zhu, Ruifeng Gao, Cong Huang, Quan Shi and Zhenquan Shi

Electronics 2024, 13(16), 3227; https://doi.org/10.3390/electronics13163227 - 14 Aug 2024

Abstract

This paper deals with the recursive state estimation issue for mobile robot localization under a dynamic event-based mechanism. To enhance the utilization of communication resources, a dynamic event-based transmission protocol is utilized to reduce unnecessary measurement transmissions by introducing an auxiliary dynamical variable [...] Read more.

This paper deals with the recursive state estimation issue for mobile robot localization under a dynamic event-based mechanism. To enhance the utilization of communication resources, a dynamic event-based transmission protocol is utilized to reduce unnecessary measurement transmissions by introducing an auxiliary dynamical variable to adjust threshold parameters. The primary objective of this paper is to develop a dynamic event-based recursive state estimation scheme for the mobile robot localization problem in the presence of the impact of the dynamic event-based mechanism such that an upper bound on the estimation error covariance is firstly guaranteed by using mathematical induction and then is locally minimized by virtue of appropriately choosing the gain parameters. Furthermore, the boundedness analysis of the estimation error is conducted by establishing an evaluation criteria in the mean-squared sense. Finally, an experimental example is conducted to verify the feasibility of the proposed mobile robot localization strategy. Full article

(This article belongs to the Special Issue Advances in Mobile Robots: Navigation, Motion Planning and Control)

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19 pages, 866 KiB

Open AccessArticle

Kidney Tumor Segmentation Based on DWR-SegFormer

by Yani Deng, Xin Liu, Lianhe Shao, Kai Wang, Xihan Wang and Quanli Gao

Electronics 2024, 13(16), 3226; https://doi.org/10.3390/electronics13163226 - 14 Aug 2024

Abstract

Kidney cancer is a malignant tumor with a high mortality rate. The accurate segmentation of tumors from computed tomography (CT) scans can assist physicians in clinical diagnosis. We introduced a new segmentation network called DWR-SegFormer to address the challenge of accurately segmenting kidney [...] Read more.

Kidney cancer is a malignant tumor with a high mortality rate. The accurate segmentation of tumors from computed tomography (CT) scans can assist physicians in clinical diagnosis. We introduced a new segmentation network called DWR-SegFormer to address the challenge of accurately segmenting kidney tumors in CT images. The method involved binarizing the label maps of clear cell renal cell carcinoma and papillary renal cell carcinoma CT images for identification, and the cancer lesion area was obtained by the label so that the model could accurately identify the area and enhance the feature extraction ability. Secondly, an optimized segmentation model combining a DWR attention mechanism and SegFormer network was constructed. MiT-B0 was used as the encoder of the model to establish long-distance feature dependencies and effectively extract feature information at different resolutions. The decoder with a multi-branch DWR module was implemented to utilize multi-scale feature information effectively and enhance segmentation accuracy. Comparing the experimental results with other existing models shows that the model significantly outperformed the comparison methods in all evaluation metrics on the CT image dataset of clear cell renal cancer. Furthermore, the experimental findings highlight the robustness of the proposed model across other datasets. Full article

(This article belongs to the Special Issue Image Segmentation)

21 pages, 6497 KiB

Open AccessArticle

The Analysis and Research of the Integrated, 30 A MOSFET Gate Driver Dedicated to High-Frequency Applications

by Piotr Legutko

Electronics 2024, 13(16), 3225; https://doi.org/10.3390/electronics13163225 - 14 Aug 2024

Abstract

This paper presents basic properties and laboratory tests of a commercial integrated high-frequency MOSFET gate driver IXRFD631 operating in the frequency range up to 30 MHz. The MOSFET driver has been tested for two operating states: in the idle state (no load) and [...] Read more.

This paper presents basic properties and laboratory tests of a commercial integrated high-frequency MOSFET gate driver IXRFD631 operating in the frequency range up to 30 MHz. The MOSFET driver has been tested for two operating states: in the idle state (no load) and at the gate load of a DE275-501N16A series MOSFET transistors. The obtained laboratory results were compared with three other commercial integrated drivers: DEIC420, DEIC515 and IXRFD630 (which are the base structures), and two previous solutions from the author (4xUCC27516 and 8xUCC27526). Additionally, this paper presents the characteristics of power losses and efficiency, measurements of switching and propagation times of the tested gate drivers. Also, this paper presents the output voltage waveforms of the integrated driver IXRFD631 for two operating states. The integrated circuit IXRFD631 of the gate driver is characterized by an efficiency of up to 70% for the tested frequency range, the power losses for two operating states (at idle state—15 W, at gate MOSFET load—43 W) and switching times of 2 ns for an operating frequency of 30 MHz. Full article

(This article belongs to the Special Issue Applications, Control and Design of Power Electronics Converters)

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16 pages, 1010 KiB

Open AccessArticle

MST: Multiscale Flow-Based Student–Teacher Network for Unsupervised Anomaly Detection

by Yi Yang, Yi Yang, Shubo Zhou, Yongbin Gao, Yadong Zhu, Xuefen Wan, Weiyu Hu and Xueqin Jiang

Electronics 2024, 13(16), 3224; https://doi.org/10.3390/electronics13163224 - 14 Aug 2024

Abstract

Student–teacher networks have shown promise in unsupervised anomaly detection; however, issues such as semantic confusion and abnormal deformations still restrict the detection accuracy. To address these issues, we propose a novel student–teacher network named MST by integrating the multistage pixel-reserving bridge (MPRB) and [...] Read more.

Student–teacher networks have shown promise in unsupervised anomaly detection; however, issues such as semantic confusion and abnormal deformations still restrict the detection accuracy. To address these issues, we propose a novel student–teacher network named MST by integrating the multistage pixel-reserving bridge (MPRB) and the spatial compression autoencoder (SCA) to the MMR network. The MPRB enhances inter-level information interaction and local feature extraction, improving the anomaly localization and reducing the false detection area. The SCA bolsters global feature extraction, making the detection boundaries of larger defects clearer. By testing our network across various datasets, our method achieves state-of-the-art (SOTA) performance on AeBAD-S, AeBAD-V, and MPDD datasets, with image-level AUROC scores of 87.5%, 78.5%, and 96.5%, respectively. Furthermore, our method also exhibits competitive performance on the widely utilized MVTec AD dataset. Full article

21 pages, 1059 KiB

Open AccessReview

A Comprehensive Survey on Machine Learning Methods for Handover Optimization in 5G Networks

by Senthil Kumar Thillaigovindhan, Mardeni Roslee, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman and Fatimah Zaharah Ali

Electronics 2024, 13(16), 3223; https://doi.org/10.3390/electronics13163223 - 14 Aug 2024

Abstract

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a [...] Read more.

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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27 pages, 626 KiB

Open AccessReview

Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database

by Bing Zhu, Zihong Zhou, Shaode Yu, Xiaokun Liang, Yaoqin Xie and Qiuirui Sun

Electronics 2024, 13(16), 3222; https://doi.org/10.3390/electronics13163222 - 14 Aug 2024

Abstract

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, [...] Read more.

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance. Full article

(This article belongs to the Special Issue Signal, Image and Video Processing: Development and Applications)

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21 pages, 3860 KiB

Open AccessArticle

FQTrack:Object Tracking Method Based on a Feature-Enhanced Memory Network and Memory Quality Selection Mechanism

by Jianwei Zhang, Mengya Zhang, Huanlong Zhang, Zengyu Cai and Liang Zhu

Electronics 2024, 13(16), 3221; https://doi.org/10.3390/electronics13163221 - 14 Aug 2024

Abstract

Visual object tracking technology is widely used in intelligent security, automatic driving and other fields, and also plays an important role in frontier fields such as human–computer interactions and virtual reality. The memory network improves the stability and accuracy of tracking by using [...] Read more.

Visual object tracking technology is widely used in intelligent security, automatic driving and other fields, and also plays an important role in frontier fields such as human–computer interactions and virtual reality. The memory network improves the stability and accuracy of tracking by using historical frame information to assist in the positioning of the current frame in object tracking. However, the memory network is still insufficient in feature mining and the accuracy and robustness of the model may be reduced when using noisy observation samples to update it. In view of the above problems, we propose a new tracking framework, which uses the attention mechanism to establish a feature-enhanced memory network and combines cross-attention to aggregate the spatial and temporal context information of the target. The former introduces spatio-temporal adaptive attention and cross-spatial attention, embeds spatial location information into channels, realizes multi-scale feature fusion, dynamically emphasizes target location information, and obtains richer feature maps. The latter guides the tracker to focus on the area with the largest amount of information in the current frame to better distinguish the foreground and background. In addition, through the memory quality selection mechanism, the accuracy and richness of the feature samples are improved, thereby enhancing the adaptability and discrimination ability of the tracking model. Experiments on benchmark test sets such as OTB2015, TrackingNet, GOT-10k, LaSOT and UAV 123 show that this method achieves comparable performance with advanced trackers. Full article

(This article belongs to the Section Artificial Intelligence)

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31 pages, 5895 KiB

Open AccessArticle

Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

by Jing Li, Baidong Feng, Le Zhang and Jin Luo

Electronics 2024, 13(16), 3220; https://doi.org/10.3390/electronics13163220 - 14 Aug 2024

Abstract

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was [...] Read more.

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle’s stability control system and greatly strengthens the driving safety of the vehicle. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

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40 pages, 1958 KiB

Open AccessArticle

BeHarmony: Blockchain-Enabled Trustworthy Communication and Legitimate Decision Making in Multi-Party Internet of Vehicles Systems

by Guodong Jin, Linyi Xu, Zihan Zhou, Qi Shi, Zihao Li, Hao Xu and Yinuo Liu

Electronics 2024, 13(16), 3219; https://doi.org/10.3390/electronics13163219 - 14 Aug 2024

Abstract

The rapid development of the Internet of Vehicles using centralized systems faces significant challenges, including reliability and security vulnerabilities and high latency. This paper introduces a blockchain-enabled authentication and communication network for scalable IoV to enhance security, reduce latency, and relieve the dependency [...] Read more.

The rapid development of the Internet of Vehicles using centralized systems faces significant challenges, including reliability and security vulnerabilities and high latency. This paper introduces a blockchain-enabled authentication and communication network for scalable IoV to enhance security, reduce latency, and relieve the dependency on centralized infrastructures. The network applies blockchain-enabled domain name services and mutual authentication for fault tolerance consensus, such as PBFT and RAFT, featuring a primary layer of road side units and edge servers for inter-vehicle communication and a sub-layer within each vehicle for intra-vehicle communication. The study evaluates various scenarios and assesses roadside unit availability based on random distribution along vehicle routes. This paper also discusses the legal issues involved in the proposed model, highlighting that the IoV system should be governed by a contract-based decentralized IoV system comprising both smart contracts and traditional contracts. This model offers a novel approach to developing a decentralized, secure, efficient, and ethical IoV ecosystem, advancing autonomous and reliable vehicular networks. Full article

(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)

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18 pages, 4018 KiB

Open AccessArticle

The Coordination Control Strategy for Improving Systems Frequency Stability Based on HVDC Quota Power Support

by Guojiang Zhang, Yongyong Jia and Qian Zhou

Electronics 2024, 13(16), 3218; https://doi.org/10.3390/electronics13163218 - 14 Aug 2024

Abstract

This paper aims to improve the recovery characteristics of the receiving grid during DC blocking failures through DC emergency power support, revealing the mechanism of improving the power angle stability of the receiving grid through timely DC power increase and decrease. Based on [...] Read more.

This paper aims to improve the recovery characteristics of the receiving grid during DC blocking failures through DC emergency power support, revealing the mechanism of improving the power angle stability of the receiving grid through timely DC power increase and decrease. Based on the transmission pattern of “north-to-south power transmission” in a certain provincial power grid, the actual maximum long-term overload capacity of multiple DC lines is calculated, considering multiple constraints such as the strength of the receiving grid, the stability limit of AC evacuation channels, and DC overload capacity. A multi-DC emergency power support strategy after DC blocking is developed, avoiding the risk of AC transmission channel overload caused by power flow transfer, reducing the impact of large-capacity DC blocking and inappropriate DC power increases on the receiving grid, and achieving coordination between AC and DC systems. The effectiveness of the proposed strategy is verified through simulation. Full article

(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology, 2nd Edition)

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22 pages, 9193 KiB

Open AccessArticle

RS-Xception: A Lightweight Network for Facial Expression Recognition

by Liefa Liao, Shouluan Wu, Chao Song and Jianglong Fu

Electronics 2024, 13(16), 3217; https://doi.org/10.3390/electronics13163217 - 14 Aug 2024

Abstract

Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom [...] Read more.

Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom effect monitoring, human–computer interaction, specialized training for athletes (such as in figure skating and rhythmic gymnastics), and actor emotion training. Recent studies have employed advanced deep learning models to address this task, though these models often encounter challenges like subpar performance and an excessive number of parameters that do not align with the requirements of FER for embedded devices. To tackle this issue, we have devised a lightweight network structure named RS-Xception, which is straightforward yet highly effective. Drawing on the strengths of ResNet and SENet, this network integrates elements from the Xception architecture. Our models have been trained on FER2013 datasets and demonstrate superior efficiency compared to conventional network models. Furthermore, we have assessed the model’s performance on the CK+, FER2013, and Bigfer2013 datasets, achieving accuracy rates of 97.13%, 69.02%, and 72.06%, respectively. Evaluation on the complex RAF-DB dataset yielded an accuracy rate of 82.98%. The incorporation of transfer learning notably enhanced the model’s accuracy, with a performance of 75.38% on the Bigfer2013 dataset, underscoring its significance in our research. In conclusion, our proposed model proves to be a viable solution for precise sentiment detection and estimation. In the future, our lightweight model may be deployed on embedded devices for research purposes. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)

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17 pages, 8750 KiB

Open AccessArticle

Distracted Driving Behavior Detection Algorithm Based on Lightweight StarDL-YOLO

by Qian Shen, Lei Zhang, Yuxiang Zhang, Yi Li, Shihao Liu and Yin Xu

Electronics 2024, 13(16), 3216; https://doi.org/10.3390/electronics13163216 - 14 Aug 2024

Abstract

Distracted driving is one of the major factors leading drivers to ignore potential road hazards. In response to the challenges of high computational complexity, limited generalization capacity, and suboptimal detection accuracy in existing deep learning-based detection algorithms, this paper introduces a novel approach [...] Read more.

Distracted driving is one of the major factors leading drivers to ignore potential road hazards. In response to the challenges of high computational complexity, limited generalization capacity, and suboptimal detection accuracy in existing deep learning-based detection algorithms, this paper introduces a novel approach called StarDL-YOLO (StarNet-detectlscd-yolo), which leverages an enhanced version of YOLOv8n. Initially, the StarNet integrated into the backbone of YOLOv8n significantly improves the feature extraction capability of the model with remarkable reduction in computational complexity. Subsequently, the Star Block is incorporated into the neck network, forming a C2f-Star module that offers lower computational cost. Additionally, shared convolution is introduced in the detection head to further reduce computational burden and parameter size. Finally, the Wise-Focaler-MPDIoU loss function is proposed to strengthen detection accuracy. The experimental results demonstrate that StarDL-YOLO significantly improves the efficiency of the distracted driving behavior detection, achieving an accuracy of 99.6% on the StateFarm dataset. Moreover, the parameter count of the model is minimized by 56.4%, and its computational load is decreased by 45.1%. Additionally, generalization experiments are performed on the 100-Driver dataset, revealing that the proposed scheme enhances generalization effectiveness compared to YOLOv8n. Therefore, this algorithm significantly reduces computational load while maintaining high reliability and generalization capability. Full article

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16 pages, 3737 KiB

Open AccessArticle

An Improved Series 36-Pulse Rectifier Based on Dual Passive Pulse-Doubling Circuit on the System DC Side

by Xiuqing Mu, Xiaoqiang Chen, Chungui Ma, Ying Wang, Tun Bai, Leijiao Ge and Xiping Ma

Electronics 2024, 13(16), 3215; https://doi.org/10.3390/electronics13163215 - 14 Aug 2024

Abstract

The series-type 12-pulse rectifier generates a large amount of harmonics in the AC side current due to the strong nonlinearity of its rectifying diodes, causing serious harmonic pollution to the power grid. This article proposes a series 36-pulse rectifier based on a DC [...] Read more.

The series-type 12-pulse rectifier generates a large amount of harmonics in the AC side current due to the strong nonlinearity of its rectifying diodes, causing serious harmonic pollution to the power grid. This article proposes a series 36-pulse rectifier based on a DC side dual passive frequency-doubling circuit to suppress the AC side current harmonics of a 12-pulse rectifier. The rectifier uses two symmetrical passive pulse multiplication circuits to regulate the circulation in the DC side circuit, increasing the output voltage and current state of the rectifier bridge, thereby increasing the number of pulses in the rectifier from 12 times to 36 times. Firstly, the working principle of the rectifier and the working mode of the dual passive frequency-doubling circuit were analyzed. Secondly, the harmonic suppression mechanism of the rectifier input current was revealed, and the frequency-doubling characteristics of the load voltage were analyzed. Finally, the correctness of the theoretical analysis was verified through a semi-physical platform. The verification and comparison results show that under the optimal conditions of the injecting transformer turns ratio, the DPPC can not only reduce the THD value of the input current by about 1/3 (from 14.87% to 4.78%) but can also increase the fluctuation frequency of the load voltage by 3 times (from 12 to 36), while improving the power quality of the AC/DC side rectifier and achieving the low harmonic operation of the rectifier. The proposed 36-pulse rectifier can effectively suppress harmonics; it has the advantages of simple structure, strong robustness, and high output voltage gain, and it is suitable for medium-voltage and high-voltage high-power rectification applications. Full article

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21 pages, 15449 KiB

Open AccessArticle

A Shared-Road-Rights Driving Strategy Based on Resolution Guidance for Right-of-Way Conflicts

by Mei Li, Guisheng Li, Chuan Sun, Junru Yang, Haoran Li, Jialin Li and Fei Li

Electronics 2024, 13(16), 3214; https://doi.org/10.3390/electronics13163214 - 14 Aug 2024

Abstract

In addressing the critical issue of right-of-way conflicts in mixed-traffic environments, this paper introduces a novel shared right-of-way driving strategy that encompasses two guiding frameworks for resolution. The first framework applies to active lane changing. Before lane changing occurs, this framework allocates the [...] Read more.

In addressing the critical issue of right-of-way conflicts in mixed-traffic environments, this paper introduces a novel shared right-of-way driving strategy that encompasses two guiding frameworks for resolution. The first framework applies to active lane changing. Before lane changing occurs, this framework allocates the right of way for autonomous vehicles (AVs). Based on the allocated right of way, the AVs decide whether to send a request for a shared right of way to relevant vehicles. To enhance lane-changing comfort, the vehicle assesses whether the variance of roll and lateral acceleration exceeds a preset threshold, ultimately deciding whether to proceed with the lane change. The second framework pertains to passive lane changing. After detecting an obstacle, this framework allocates the right of way. The AVs calculate acceleration based on their speed and distance from the obstacle, using this information to determine whether to change lanes or decelerate in order to avoid the obstacle. If lane changing is chosen, further evaluation is necessary. Based on the allocated right of way, the AVs decide whether to request a shared right of way from relevant vehicles. To improve lane-changing comfort, the AVs compare the variance of roll and lateral acceleration with that of pitch and longitudinal acceleration, and then they decide whether to proceed with the lane change. The proposed strategy has been validated in various scenarios, including high-speed (105 km/h), low speed (13 km/h), and general scenarios with AVs and obstacles at a distance of 125 m. The results show that the strategy effectively functions in both high-speed and low-speed scenarios. Full article

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22 pages, 750 KiB

Open AccessArticle

S-ZAC: Hardening Access Control of Service Mesh Using Intel SGX for Zero Trust in Cloud

by Changhee Han, Taehun Kim, Woomin Lee and Youngjoo Shin

Electronics 2024, 13(16), 3213; https://doi.org/10.3390/electronics13163213 - 14 Aug 2024

Abstract

In cloud services, the zero-trust security paradigm has emerged as a key strategy to reduce the large attack surface created by the complexity of cloud systems. Service mesh is a popular practice to realize the zero-trust architecture, which relies heavily on network access [...] Read more.

In cloud services, the zero-trust security paradigm has emerged as a key strategy to reduce the large attack surface created by the complexity of cloud systems. Service mesh is a popular practice to realize the zero-trust architecture, which relies heavily on network access control to achieve the desired security. Building a service mesh-based solution in the cloud is not straightforward because privileged adversaries (e.g., malicious cloud insiders) can easily compromise the control plane where the access control function is implemented. In this paper, we propose S-ZAC, an access control hardening technique for service mesh-based solutions in the cloud. S-ZAC uses Intel SGX to provide a trusted execution environment for the control plane, which is responsible for enforcing access control for the service mesh. By isolating all access-control-related functions within an SGX enclave, S-ZAC ensures high resilience of the service mesh solution even in the presence of privileged adversaries. Due to the design limitations of SGX, implementing S-ZAC in the cloud for zero trust faces several challenges that can lead to serious scalability and failover issues. The first challenge is to establish secure communication channels between the S-ZAC components, even in the presence of privileged attackers. The second challenge is the limited memory capacity of the SGX enclave. Finally, the third challenge is that the inherent design of SGX does not support persistent enclave states, meaning that any state of running enclaves is volatile. We address these challenges by proposing our novel solutions. By implementing a prototype of S-ZAC, we evaluate its performance in terms of security and performance. The evaluation results validate the effectiveness of S-ZAC to enhance the security of the service mesh control plane in cloud environments. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 1090 KiB

Open AccessArticle

A Topic Modeling Based on Prompt Learning

by Mingjie Qiu, Wenzhong Yang, Fuyuan Wei and Mingliang Chen

Electronics 2024, 13(16), 3212; https://doi.org/10.3390/electronics13163212 - 14 Aug 2024

Abstract

Most of the existing topic models are based on the Latent Dirichlet Allocation (LDA) or the variational autoencoder (VAE), but these methods have inherent flaws. The a priori assumptions of LDA on documents may not match the actual distribution of the data, and [...] Read more.

Most of the existing topic models are based on the Latent Dirichlet Allocation (LDA) or the variational autoencoder (VAE), but these methods have inherent flaws. The a priori assumptions of LDA on documents may not match the actual distribution of the data, and VAE suffers from information loss during the mapping and reconstruction process, which tends to affect the effectiveness of topic modeling. To this end, we propose a Prompt Topic Model (PTM) utilizing prompt learning for topic modeling, which circumvents the structural limitations of LDA and VAE, thereby overcoming the deficiencies of traditional topic models. Additionally, we develop a prompt word selection method that enhances PTM’s efficiency in performing the topic modeling task. Experimental results demonstrate that the PTM surpasses traditional topic models on three public datasets. Ablation experiments further validate that our proposed prompt word selection method enhances the PTM’s effectiveness in topic modeling. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 10613 KiB

Open AccessArticle

Domain-Adaptive Framework for ACL Injury Diagnosis Utilizing Contrastive Learning Techniques

by Weiqiang Liu, Weilun Lin, Zefeng Zhuang and Kehua Miao

Electronics 2024, 13(16), 3211; https://doi.org/10.3390/electronics13163211 - 14 Aug 2024

Abstract

In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques [...] Read more.

In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques for ACL diagnosis work well on single datasets, research on cross-domain data transfer is still lacking. Building strong domain-adaptive diagnostic models requires addressing domain disparities in ACL magnetic resonance imaging (MRI) from different hospitals and making efficient use of multiple ACL datasets. This work uses the publicly available KneeMRI dataset from Croatian hospitals coupled with the publicly available MRnet dataset from Stanford University to investigate domain adaptation and transfer learning models. First, an optimized model efficiently screens training data in the source domain to find unusually misclassified occurrences. Subsequently, before being integrated into the contrastive learning module, a target domain feature extraction module processes features of target domain samples to improve extraction efficiency. By using contrastive learning between positive and negative sample pairs from source and target domains, this method makes domain adaptation easier and improves the efficacy of ACL auxiliary diagnostic models. Utilizing a spatially augmented ResNet-18 backbone network, the suggested approach produces notable enhancements in experimentation. To be more precise, the AUC for transfer learning improved by 3.5% from MRnet to KneeMRI and by 2.5% from KneeMRI to MRnet (from 0.845 to 0.870). This method shows how domain transfer can be used to improve diagnostic accuracy on a variety of datasets and effectively progresses the training of a strong ACL auxiliary diagnostic model. Full article

(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)

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32 pages, 12646 KiB

Open AccessReview

A Bibliometric Review of Intrusion Detection Research in IoT: Evolution, Collaboration, and Emerging Trends

by Nikolaj Goranin, Simran Kaur Hora and Habil Antanas Čenys

Electronics 2024, 13(16), 3210; https://doi.org/10.3390/electronics13163210 - 13 Aug 2024

Abstract

As the IoT market continues to rapidly expand, ensuring the security of IoT systems becomes increasingly critical. This paper aims to identify emerging trends and technologies in IoT intrusion detection. A bibliometric analysis of research trends in IoT intrusion detection, leveraging data from [...] Read more.

As the IoT market continues to rapidly expand, ensuring the security of IoT systems becomes increasingly critical. This paper aims to identify emerging trends and technologies in IoT intrusion detection. A bibliometric analysis of research trends in IoT intrusion detection, leveraging data from the Web of Science (WoS) repository, is conducted to understand the landscape of publications in this field. The analysis reveals a significant increase in publications on intrusion detection in IoT, indicating growing research interest. Research articles are the leading category of publications, and the analysis also highlights the collaborative linkages among authors, institutions, and nations. Co-occurrence analysis and citation analysis provide insights into the relationships among keywords and the impact of publications. The study also identifies keyword and publication citation burst detection, with recommendations for future research focusing on advanced machine learning techniques to enhance intrusion/anomaly detection. This comprehensive analysis offers valuable guidance for diverse and extensive applications in IoT intrusion detection. Full article

(This article belongs to the Special Issue Futuristic Security and Privacy in 6G-Enabled IoT)

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