IEEE Access最新文献

{"title":"Simple SLL Reduction Method for an SIW Longitudinal Slot Array Antenna","authors":"Deokjin Seo;Junmo Choi;Jongin Ryu;Kyung-Young Jung","doi":"10.1109/ACCESS.2024.3473910","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473910","url":null,"abstract":"The deployment of Ka-band radar necessitates addressing increased losses due to rain and free-space propagation, in addition to achieving low sidelobe levels (SLL). Substrate integrated waveguide (SIW) slot antennas, with their high-power handling capability and low loss, present a practical solution for Ka-band radar applications. However, previous SLL reduction methods for SIW longitudinal slot array antennas involves high design complexity or degrade the antenna’s radiation efficiency. In this work, we introduce a novel SLL reduction method for an SIW longitudinal slot array antenna using only one design variable—slot offset—to map electric field amplitude to a reference distribution. Our method effectively reduces SLL while maintaining radiation efficiency. The proposed antenna was fabricated and measured, showing good agreement with simulations. Compared to previous methods, our approach is simpler, advancing SIW slot array antenna design and SLL reduction.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146359-146365"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Xducation of Things (XoT): Harnessing AI and Edge Computing to Educate All Things","authors":"Rio Nurtantyana;Wu-Yuin Hwang;Uun Hariyanti","doi":"10.1109/ACCESS.2024.3474015","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474015","url":null,"abstract":"Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthingsand EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthingsin the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthingswith SQA-Forwarding significantly improved learners’ writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147138-147155"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-Based Plant Identification Using Multi-Level Classification","authors":"Afagh Mohagheghi;Mehrdad Moallem","doi":"10.1109/ACCESS.2024.3474613","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474613","url":null,"abstract":"Accurate plant identification is critical for applications such as automated agriculture and plant monitoring systems. However, traditional classification methods often face challenges in balancing accuracy and computational efficiency, particularly when handling large datasets or real-time processing. This research aims to develop a classification scheme that efficiently identifies plant types based on color and shape attributes, achieving high accuracy with minimal computational complexity. To address this, we propose a two-level classification approach using a Naive Bayes classifier in a hierarchical structure. The first stage utilizes simple color features to categorize the majority of images with high accuracy and low computational overhead. In cases where classification remains uncertain, the second stage extracts additional color and shape attributes, offering a more refined analysis of complex samples. The scheme is implemented within an Internet of Things (IoT)-enabled data acquisition framework, enabling real-time image data collection. The system was evaluated using four types of artificial plants placed in a growth chamber equipped with image sensors and LED lighting, with data processed through a cloud service. The results demonstrate that the two-level classifier outperforms single-level approaches, maintaining high accuracy by deferring more complex samples to the second stage without significantly increasing computational costs. This hierarchical classification scheme successfully balances efficiency and accuracy, making it well-suited for large-scale applications such as smart greenhouses, where reliable and rapid plant classification is essential.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146366-146375"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Insulators Detection Accuracy via Image Enhancement Techniques: Case of Indigenous Aerial Image Dataset","authors":"Shafi Muhammad Jiskani;Tanweer Hussain;Anwar Ali Sahito;Faheemullah Shaikh;Laveet Kumar","doi":"10.1109/ACCESS.2024.3474255","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474255","url":null,"abstract":"The challenging task of insulator monitoring through aerial images is addressed in high voltage transmission network and highlights the limitations of traditional human patrolling with emphasize on utilization of unmanned aerial vehicles UAVs utilizing machine learning algorithms. This research has been accomplished by creating indigenous dataset of 500kV transmission network of National Transmission and Despatch Center Limited (NTDCL). 608 original images were captured in diverse lighting and topographical conditions which were then augmented to 3618 images. To improve the detection accuracy of YOLOv8s algorithm in aerial images, HSV and CLAHE image enhancement techniques were employed to improve the visual feature of insulator with suppressed noise. YOLOv8s algorithm with image enhancement has improved detection accuracy from 88% to 95% demonstrating the effectiveness of integrating image enhancement technique for insulator monitoring, offering promising improvement in maintenance practices and operational reliability of transmission lines.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"145582-145589"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Classification Method for Helmet Wearing State Based on Progressive Multi-Granularity Training Strategy","authors":"Yi-Jia Zhang;Fusu Xiao;Zhe-Ming Lu","doi":"10.1109/ACCESS.2024.3474433","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474433","url":null,"abstract":"In many construction sites, whether to wear the safety helmet directly affects the life safety of workers. Therefore, monitoring the wearing state of safety helmets has become an important auxiliary means of construction safety. However, most current safety helmet wearing state monitoring algorithms only distinguish workers who are wearing safety helmets from those who are not, which has high detection limitations and algorithm performance needs to be improved. In this paper, we innovatively apply fine-grained classification algorithms to classify the wearing state of safety helmets, and propose a progressive multi-granularity training strategy based safety helmet wearing state classification algorithm PMG-Helmet (Progressive Multi-granularity for Helmet, PMG-Helmet) for the six classification dataset of safety helmet wearing state. This algorithm achieves multi-granularity classification of helmet wearing state through a puzzle generator and a progressive training strategy, and introduces the MC-Loss(Mutual Channel Loss) function designed specifically for fine-grained classification tasks to improve algorithm performance. In the algorithm inference stage, this paper normalized the weights of the outputs of each stage of the PMG-Helmet algorithm, resulting in better combination accuracy. The experimental results show that the accuracy of this algorithm on the six classification dataset is 93.36%. Specifically, in order to further investigate the effectiveness of the algorithm, this study conducted separate studies on the finer subcategories of “wearing the helmet correctly” and “wearing the helmet but not fastening the chin strap” during the experimental phase, achieving an accuracy of 90.11%.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146397-146408"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing a Two Planes Model to Rectify Documents With a Single Arbitrary Crease","authors":"Aleksandr Ershov;Daniil Tropin;Danil Kazimirov;Konstantin Bulatov;Dmitry Nikolaev","doi":"10.1109/ACCESS.2024.3474099","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474099","url":null,"abstract":"Document image rectification problem is crucial in document analysis. Most of the current state-of-the-art methods addressing it are data-driven and rely on neural network approaches. However, despite satisfactory rectifications, such methods’ time performance is poor, making them unsuitable for mobile on-device acquisition. The present work concentrates on a specific (but common) case of document physical distortion – the documents with a single crease. We investigate the properties of a surface comprised of two planes captured by a pinhole camera. Namely, we provide the methods to obtain the transformation between such an image and the template image having successfully localized the document in a frame. It can be utilized in on-device recognition systems: it takes only 3 ms to estimate transformation parameters and about a quarter of a second to rectify an image on a smartphone CPU. We propose a novel dataset FDI-AC containing 200 real images of documents with a single crease in different positions. We conduct experiments comparing our approach with the current state-of-the-art setting a baseline performance on FDI-AC. These experiments show that the proposed algorithm outperforms image rectification transformer network GeoTr in rectification accuracy and time performance.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147073-147086"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme Demands","authors":"Dohyun Lee;Kyoungok Kim","doi":"10.1109/ACCESS.2024.3474017","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474017","url":null,"abstract":"Boosting algorithms are widely used for predicting demand in bike-sharing systems (BSSs). However, these systems often encounter sudden spikes in demand (extreme demand). Ordinary boosting algorithms tend to be biased toward extreme demands, leading to increased prediction errors in other scenarios. Noise-robust boosting algorithms perform well with normal samples; however, for normal samples in datasets containing extreme demands, their accuracy remains poor for extreme demand samples. To address these limitations, we propose a novel boosting algorithm, AdaBoost.RDT, which integrates adaptive boosting with a residual-based decision tree. Our approach aims to enhance prediction accuracy for extreme demand scenarios without compromising performance in normal situations. By incorporating a decision tree (DT) model at each boosting iteration to predict residuals from the base model, we effectively identify and improve predictions for underestimated extreme demands. AdaBoost.RDT was compared with six boosting algorithms, including noise-robust variants, using Seoul Bike and Daejeon Bike data. Experimental results demonstrated that the DT model within AdaBoost.RDT effectively distinguished between over- and under-estimated samples, significantly reducing prediction errors for extreme demand scenarios with compromised accuracy for very low demands. On the stance in operating a shared bicycle service, it is important to alleviate the customer dissatisfaction caused by not being able to rent bicycle encouraged by extreme events. Therefore, it should be achieved even if it requires compromised accuracy for very low demands.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144316-144336"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Configurable Arithmetic Core Architecture for RNS-CKKS Homomorphic Encryption","authors":"Chulwoo Lee;Hanyoung Lee;Ardianto Satriawan;Hanho Lee","doi":"10.1109/ACCESS.2024.3473903","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473903","url":null,"abstract":"Fully Homomorphic Encryption (FHE) provides privacy-preserving applications due to its ability to perform arithmetic computations such as addition and multiplication on encrypted data without decrypting them first. However, there are bottlenecks to its practical applications because of its large data size, significant computational power, and memory usage requirements. One of the bottlenecks is key-switching, which is required when performing homomorphic multiplications. In the CKKS scheme, when multiplying two ciphertexts. Initially, both ciphertexts consist of two polynomial elements multiplied by dyadic multiplication. Consequently, the resulting ciphertext consists of three elements. An operation known as key-switching is required to relinearize the ciphertext from three to two elements and make it decryptable with the initial secret key. However, it is a computationally expensive operation, with the number theoretic transform (NTT) and its inverse (INTT) being the most time and resource-consuming parts. To address the problem, this technical report proposes a configurable arithmetic core (CAC) hardware accelerator that can be used for key-switching in the CKKS scheme. Our architecture offers a configurable arithmetic core that can be configured for NTT, INTT, and multiply-and-accumulate (MAC) operations. We implemented our design in the AMD Xilinx Alveo U250 FPGA platform. We then use this architecture to perform key-switching operations in the CKKS scheme. As a \u0000<inline-formula> <tex-math>$2^{16}$ </tex-math></inline-formula>\u0000 NTT/INTT accelerator, our design performs, when compared to classical architecture, our design performs 11.33 times faster. Meanwhile, compared to the state-of-the-art architecture, it performs 1.07 times faster. Our design can also run at a higher frequency than others. As a key-switching accelerator, compared to the CPU implementation by OpenFHE, our design implementation in FPGA gains about 1600 to 2700 times speed-up. Compared to other FPGA design, our key-switching accelerator offers more configurability on the multiplicative level.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147220-147234"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamental Coordinate Space for Object 6D Pose Estimation","authors":"Boyan Wan;Chen Zhang","doi":"10.1109/ACCESS.2024.3473936","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473936","url":null,"abstract":"Estimating the 6D pose of objects, including symmetric ones, is a critical task in computer vision and robotics. Previous correspondence-based methods faced challenges with symmetric objects due to the ambiguities they introduce, necessitating the learning of complex one-to-many correspondences between camera space and object coordinate space. To address this issue, we introduce a novel approach that leverages the concept of fundamental coordinate space. This approach transforms one-to-many correspondences into precise one-to-one correspondences, significantly simplifying the network’s learning process and enhancing its pose estimation performance. Our approach begins by identifying an object’s fundamental coordinate space through a comprehensive pipeline. Subsequently, we develop a coordinate-based attention network to predict dense correspondences between the camera and the fundamental coordinate space. The network employs a fusion module based on attention operations to effectively integrate geometry and texture information at arbitrary query points around the object. Experimental results show that our method surpasses previous state-of-the-art models on both T-LESS and NOCS-REAL datasets, improving the ARMSSD score by 1.4 percentage points on T-LESS and the \u0000<inline-formula> <tex-math>$5^{circ }5$ </tex-math></inline-formula>\u0000cm score by 6 percentage points on NOCS-REAL, demonstrating its superior performance in 6D pose estimation tasks. Our code is available at \u0000<uri>https://github.com/wanboyan/FCS</uri>\u0000.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146430-146440"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Strategy for Chat Transcript Summarization","authors":"Pratik K. Biswas","doi":"10.1109/ACCESS.2024.3473968","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473968","url":null,"abstract":"Text summarization is the process of condensing a piece of text to fewer sentences, while still preserving its content. Chat transcript, in this context, is a textual copy of a digital or online conversation between a customer (caller) and agent(s). This paper presents an indigenously (locally) developed hybrid method that first combines extractive (unsupervised) and abstractive (supervised) summarization techniques in compressing ill-punctuated or unpunctuated chat transcripts to produce more readable punctuated summaries and then optimizes the overall quality of summarization through reinforcement learning. Extensive testing, evaluations, comparisons, and validation have demonstrated the efficacy of this approach for large-scale deployment of chat transcript summarization, in the absence of manually generated reference (annotated) summaries.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146620-146634"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}