Pub Date : 2024-08-09DOI: 10.3390/electronics13163150
N.M. Sirakov, A. Bowden
The main objective of this paper is to present a repository of image databases whose features are augmented with embedded vector field (VF) features. The repository is designed to provide the user with image databases that enhance machine learning (ML) classification. Also, six VFs are provided, and the user can embed them into her/his own image database with the help of software named ELPAC. Three of the VFs generate real-shaped singular points (SPs): springing, sinking, and saddle. The other three VFs generate seven kinds of SPs, which include the real-shaped SPs and four complex-shaped SPs: repelling and attracting (out and in) spirals and clockwise and counterclockwise orbits (centers). Using the repository, this work defines the locations of the SPs according to the image objects and the mappings between the SPs’ shapes if separate VFs are embedded into the same image. Next, this paper produces recommendations for the user on how to select the most appropriate VF to be embedded in an image database so that the augmented SP shapes enhance ML classification. Examples of images with embedded VFs are shown in the text to illustrate, support, and validate the theoretical conclusions. Thus, the contributions of this paper are the derivation of the SP locations in an image; mappings between the SPs of different VFs; and the definition of an imprint of an image and an image database in a VF. The advantage of classifying an image database with an embedded VF is that the new database enhances and improves the ML classification statistics, which motivates the design of the repository so that it contains image features augmented with VF features.
{"title":"Image Databases with Features Augmented with Singular-Point Shapes to Enhance Machine Learning","authors":"N.M. Sirakov, A. Bowden","doi":"10.3390/electronics13163150","DOIUrl":"https://doi.org/10.3390/electronics13163150","url":null,"abstract":"The main objective of this paper is to present a repository of image databases whose features are augmented with embedded vector field (VF) features. The repository is designed to provide the user with image databases that enhance machine learning (ML) classification. Also, six VFs are provided, and the user can embed them into her/his own image database with the help of software named ELPAC. Three of the VFs generate real-shaped singular points (SPs): springing, sinking, and saddle. The other three VFs generate seven kinds of SPs, which include the real-shaped SPs and four complex-shaped SPs: repelling and attracting (out and in) spirals and clockwise and counterclockwise orbits (centers). Using the repository, this work defines the locations of the SPs according to the image objects and the mappings between the SPs’ shapes if separate VFs are embedded into the same image. Next, this paper produces recommendations for the user on how to select the most appropriate VF to be embedded in an image database so that the augmented SP shapes enhance ML classification. Examples of images with embedded VFs are shown in the text to illustrate, support, and validate the theoretical conclusions. Thus, the contributions of this paper are the derivation of the SP locations in an image; mappings between the SPs of different VFs; and the definition of an imprint of an image and an image database in a VF. The advantage of classifying an image database with an embedded VF is that the new database enhances and improves the ML classification statistics, which motivates the design of the repository so that it contains image features augmented with VF features.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163152
Yiyao Chu, Qinggong Zheng
Efficient pilotage planning is essential, particularly due to the increasing demand for skilled pilots amid frequent vessel traffic. Addressing pilot shortages and ensuring navigational safety, this study presents an innovative pilot-ASV scheduling strategy. This approach utilizes autonomous surface vehicles (ASVs) to assist or replace junior pilots in specific tasks, thereby alleviating pilot resource constraints and upholding safety standards. We develop a comprehensive mathematical model that accommodates pilot work time windows, various pilot levels, and ASV battery limitations. An improved artificial bee colony algorithm is proposed to solve this model effectively, integrating breadth-first and depth-first search strategies to enhance solution quality and efficiency uniquely. Extensive numerical experiments corroborate the model’s effectiveness, showing that our integrated optimization approach decreases vessel waiting times by an average of 9.18% compared to traditional methods without ASV integration. The findings underscore the potential of pilot-ASV scheduling to significantly improve both the efficiency and safety of vessel pilotages.
高效的引航规划至关重要，尤其是在船舶交通频繁的情况下，对熟练引航员的需求与日俱增。为解决引航员短缺问题并确保航行安全，本研究提出了一种创新的引航员-ASV 调度策略。这种方法利用自动水面航行器（ASV）协助或替代初级引航员执行特定任务，从而缓解引航员资源紧张状况并维护安全标准。我们建立了一个全面的数学模型，该模型考虑到了飞行员工作时间窗口、不同的飞行员级别以及 ASV 电池的限制。为有效求解该模型，我们提出了一种改进的人工蜂群算法，该算法整合了广度优先和深度优先搜索策略，从而独特地提高了求解质量和效率。广泛的数值实验证实了该模型的有效性，结果表明，与未集成 ASV 的传统方法相比，我们的集成优化方法平均减少了 9.18% 的船舶等待时间。这些发现强调了引航员-ASV 调度在显著提高船舶引航效率和安全性方面的潜力。
{"title":"Optimizing Pilotage Efficiency with Autonomous Surface Vehicle Assistance","authors":"Yiyao Chu, Qinggong Zheng","doi":"10.3390/electronics13163152","DOIUrl":"https://doi.org/10.3390/electronics13163152","url":null,"abstract":"Efficient pilotage planning is essential, particularly due to the increasing demand for skilled pilots amid frequent vessel traffic. Addressing pilot shortages and ensuring navigational safety, this study presents an innovative pilot-ASV scheduling strategy. This approach utilizes autonomous surface vehicles (ASVs) to assist or replace junior pilots in specific tasks, thereby alleviating pilot resource constraints and upholding safety standards. We develop a comprehensive mathematical model that accommodates pilot work time windows, various pilot levels, and ASV battery limitations. An improved artificial bee colony algorithm is proposed to solve this model effectively, integrating breadth-first and depth-first search strategies to enhance solution quality and efficiency uniquely. Extensive numerical experiments corroborate the model’s effectiveness, showing that our integrated optimization approach decreases vessel waiting times by an average of 9.18% compared to traditional methods without ASV integration. The findings underscore the potential of pilot-ASV scheduling to significantly improve both the efficiency and safety of vessel pilotages.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163148
Omer Tariq, Muhammad Bilal Akram Dastagir, Dongsoo Han
With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs.
{"title":"Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations","authors":"Omer Tariq, Muhammad Bilal Akram Dastagir, Dongsoo Han","doi":"10.3390/electronics13163148","DOIUrl":"https://doi.org/10.3390/electronics13163148","url":null,"abstract":"With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163151
Siwei Wei, Yang Yang, Donghua Liu, Ke Deng, Chunzhi Wang
Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness.
{"title":"Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow Forecasting","authors":"Siwei Wei, Yang Yang, Donghua Liu, Ke Deng, Chunzhi Wang","doi":"10.3390/electronics13163151","DOIUrl":"https://doi.org/10.3390/electronics13163151","url":null,"abstract":"Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163147
Na Jin Seo, Molly Brinkhoff, Savannah Fredendall, Patricia Coker-Bolt, Kelly McGloon, Elizabeth Humanitzki
TheraBracelet is a peripheral vibrotactile stimulation applied to affected upper extremities via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine the feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently, except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with a focus on the potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy.
{"title":"The Use of TheraBracelet Upper Extremity Vibrotactile Stimulation in a Child with Cerebral Palsy—A Case Report","authors":"Na Jin Seo, Molly Brinkhoff, Savannah Fredendall, Patricia Coker-Bolt, Kelly McGloon, Elizabeth Humanitzki","doi":"10.3390/electronics13163147","DOIUrl":"https://doi.org/10.3390/electronics13163147","url":null,"abstract":"TheraBracelet is a peripheral vibrotactile stimulation applied to affected upper extremities via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine the feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently, except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with a focus on the potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163146
Yang Liu, Yuanjun Zhu, Zhi Wang, Xuejun Zhang, Yan Li
The journal retracts the article, “Ground Risk Estimation of Unmanned Aerial Vehicles Based on Probability Approximation for Impact Positions with Multi-Uncertainties” [...]
该杂志撤销了文章《基于多不确定性影响位置概率逼近的无人驾驶飞行器地面风险估计》 [...]
{"title":"RETRACTED: Liu et al. Ground Risk Estimation of Unmanned Aerial Vehicles Based on Probability Approximation for Impact Positions with Multi-Uncertainties. Electronics 2023, 12, 829","authors":"Yang Liu, Yuanjun Zhu, Zhi Wang, Xuejun Zhang, Yan Li","doi":"10.3390/electronics13163146","DOIUrl":"https://doi.org/10.3390/electronics13163146","url":null,"abstract":"The journal retracts the article, “Ground Risk Estimation of Unmanned Aerial Vehicles Based on Probability Approximation for Impact Positions with Multi-Uncertainties” [...]","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3390/electronics13163149
Liefa Liao, Shouluan Wu, Chao Song, Jianglong Fu
Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection.
{"title":"PH-CBAM: A Parallel Hybrid CBAM Network with Multi-Feature Extraction for Facial Expression Recognition","authors":"Liefa Liao, Shouluan Wu, Chao Song, Jianglong Fu","doi":"10.3390/electronics13163149","DOIUrl":"https://doi.org/10.3390/electronics13163149","url":null,"abstract":"Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/electronics13163140
Yi Gu, Liang Zhao, Tianze Liu, Shaoen Wu
Federated learning is a novel paradigm allowing the training of a global machine-learning model on distributed devices. It shares model parameters instead of private raw data during the entire model training process. While federated learning enables machine learning processes to take place collaboratively on Internet of Things (IoT) devices, compared to data centers, IoT devices with limited resource budgets typically have less security protection and are more vulnerable to potential thermal stress. Current research on the evaluation of federated learning is mainly based on the simulation of multi-clients/processes on a single machine/device. However, there is a gap in understanding the performance of federated learning under thermal stress in real-world distributed low-power heterogeneous IoT devices. Our previous work was among the first to evaluate the performance of federated learning under thermal stress on real-world IoT-based distributed systems. In this paper, we extended our work to a larger scale of heterogeneous real-world IoT-based distributed systems to further evaluate the performance of federated learning under thermal stress. To the best of our knowledge, the presented work is among the first to evaluate the performance of federated learning under thermal stress on real-world heterogeneous IoT-based systems. We conducted comprehensive experiments using the MNIST dataset and various performance metrics, including training time, CPU and GPU utilization rate, temperature, and power consumption. We varied the proportion of clients under thermal stress in each group of experiments and systematically quantified the effectiveness and real-world impact of thermal stress on the low-end heterogeneous IoT-based federated learning system. We added 67% more training epochs and 50% more clients compared with our previous work. The experimental results demonstrate that thermal stress is still effective on IoT-based federated learning systems as the entire global model and device performance degrade when even a small ratio of IoT devices are being impacted. Experimental results have also shown that the more influenced client under thermal stress within the federated learning system (FLS) tends to have a more major impact on the performance of FLS under thermal stress.
{"title":"Evaluation of Thermal Stress on Heterogeneous IoT-Based Federated Learning","authors":"Yi Gu, Liang Zhao, Tianze Liu, Shaoen Wu","doi":"10.3390/electronics13163140","DOIUrl":"https://doi.org/10.3390/electronics13163140","url":null,"abstract":"Federated learning is a novel paradigm allowing the training of a global machine-learning model on distributed devices. It shares model parameters instead of private raw data during the entire model training process. While federated learning enables machine learning processes to take place collaboratively on Internet of Things (IoT) devices, compared to data centers, IoT devices with limited resource budgets typically have less security protection and are more vulnerable to potential thermal stress. Current research on the evaluation of federated learning is mainly based on the simulation of multi-clients/processes on a single machine/device. However, there is a gap in understanding the performance of federated learning under thermal stress in real-world distributed low-power heterogeneous IoT devices. Our previous work was among the first to evaluate the performance of federated learning under thermal stress on real-world IoT-based distributed systems. In this paper, we extended our work to a larger scale of heterogeneous real-world IoT-based distributed systems to further evaluate the performance of federated learning under thermal stress. To the best of our knowledge, the presented work is among the first to evaluate the performance of federated learning under thermal stress on real-world heterogeneous IoT-based systems. We conducted comprehensive experiments using the MNIST dataset and various performance metrics, including training time, CPU and GPU utilization rate, temperature, and power consumption. We varied the proportion of clients under thermal stress in each group of experiments and systematically quantified the effectiveness and real-world impact of thermal stress on the low-end heterogeneous IoT-based federated learning system. We added 67% more training epochs and 50% more clients compared with our previous work. The experimental results demonstrate that thermal stress is still effective on IoT-based federated learning systems as the entire global model and device performance degrade when even a small ratio of IoT devices are being impacted. Experimental results have also shown that the more influenced client under thermal stress within the federated learning system (FLS) tends to have a more major impact on the performance of FLS under thermal stress.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/electronics13163137
Mahmudul Hasan, Md. Kamal Uddin, R. Suzuki, Yoshinori Kuno, Yoshinori Kobayashi
PerFication is a person identification technique that uses a 2D LiDAR sensor in a customized dataset KoLaSu (Kobayashi Laboratory of Saitama University). Video-based recognition systems are highly effective and are now at the forefront of research. However, it experiences bottlenecks. New inventions can cause embarrassing situations, settings, and momentum. To address the limitations of technology, one must introduce a new technology to enhance it. Using biometric characteristics are highly reliable and valuable methods for identifying individuals. Most approaches depend on close interactions with the subject. A gait is the walking pattern of an individual. Most research on identifying individuals based on their walking patterns is conducted using RGB or RGB-D cameras. Only a limited number of studies utilized LiDAR data. Working with 2D LiDAR imagery for individual tracking and identification is excellent in situations where video monitoring is ineffective, owing to environmental challenges such as disasters, smoke, occlusion, and economic constraints. This study presented an extensive analysis of 2D LiDAR data using a meticulously created dataset and a modified residual neural network. In this paper, an alternative method of person identification is proposed that circumvents the limitations of video cameras in terms of capturing difficulties. An individual is precisely identified by the system through the utilization of ankle-level 2D LiDAR data. Our LiDAR-based detection system offers a unique method for person identification in modern surveillance systems, with a painstaking dataset, remarkable results, and a break from traditional camera setups. We focused on demonstrating the cost-effectiveness and durability of LiDAR sensors by utilizing 2D sensors in our research.
{"title":"PerFication: A Person Identifying Technique by Evaluating Gait with 2D LiDAR Data","authors":"Mahmudul Hasan, Md. Kamal Uddin, R. Suzuki, Yoshinori Kuno, Yoshinori Kobayashi","doi":"10.3390/electronics13163137","DOIUrl":"https://doi.org/10.3390/electronics13163137","url":null,"abstract":"PerFication is a person identification technique that uses a 2D LiDAR sensor in a customized dataset KoLaSu (Kobayashi Laboratory of Saitama University). Video-based recognition systems are highly effective and are now at the forefront of research. However, it experiences bottlenecks. New inventions can cause embarrassing situations, settings, and momentum. To address the limitations of technology, one must introduce a new technology to enhance it. Using biometric characteristics are highly reliable and valuable methods for identifying individuals. Most approaches depend on close interactions with the subject. A gait is the walking pattern of an individual. Most research on identifying individuals based on their walking patterns is conducted using RGB or RGB-D cameras. Only a limited number of studies utilized LiDAR data. Working with 2D LiDAR imagery for individual tracking and identification is excellent in situations where video monitoring is ineffective, owing to environmental challenges such as disasters, smoke, occlusion, and economic constraints. This study presented an extensive analysis of 2D LiDAR data using a meticulously created dataset and a modified residual neural network. In this paper, an alternative method of person identification is proposed that circumvents the limitations of video cameras in terms of capturing difficulties. An individual is precisely identified by the system through the utilization of ankle-level 2D LiDAR data. Our LiDAR-based detection system offers a unique method for person identification in modern surveillance systems, with a painstaking dataset, remarkable results, and a break from traditional camera setups. We focused on demonstrating the cost-effectiveness and durability of LiDAR sensors by utilizing 2D sensors in our research.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/electronics13163144
Liyuan Zhang, Xiangyu Sun, Xianghua Ma, Kaitao Hu
Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction.
{"title":"A New Entity Relationship Extraction Method for Semi-Structured Patent Documents","authors":"Liyuan Zhang, Xiangyu Sun, Xianghua Ma, Kaitao Hu","doi":"10.3390/electronics13163144","DOIUrl":"https://doi.org/10.3390/electronics13163144","url":null,"abstract":"Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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