检索结果-内蒙古大学图书馆

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Sato, Shun Yasuda, Yuto Ohara, Ryotaro Hamabe, Riku Genda, Takayuki Imanaka, Shoya Izumi, Shintaro Kawaguchi, Hiroshi Kobe University Graduate School of System Informatics Kobe Japan Kobe University Department of Computer Science and Systems Engineering Kobe Japan Kobe University Graduate School of Science Technology and Innovation Kobe Japan

ISBN: (纸本)9798350346459

In bathrooms, a fall can lead to severe injuries or even drowning. While taking preventive measures against such accidents is crucial, it's equally important to swiftly detect and request rescue when they occur. In such scenarios, knowing the exact location of the person in the bathroom becomes critical. However, concerns related to privacy and costs make the use of cameras and lidar impractical. To address these issues, we propose a method for estimating the 3D location of a person within the bathroom using ultrasound correlation values as input for a deep neural network. To validate our approach, we conduct experiments using two sensors in this study. The results are promising, as we achieved a mean square error of just 10.48 cm. © 2023 IEEE.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps

引用

Journal of Educational Computing Research 2024年第5期62卷 1327-1357页

作者： Sandra, Randi Proska Hwang, Wu-Yuin Zafirah, Afifah Hariyanti, Uun Engkizar, Engkizar Hadi, Ahmaddul Fauzan, Ahmad Universitas Negeri Padang West Sumatra Indonesia Graduate Institute of Network Learning Technology National Central University Taoyuan Taiwan College of Science and Engineering National Dong Hwa University Hualien Taiwan Department of Information System Faculty of Computer Science Universitas Brawijaya East Java Indonesia

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group (N = 30) and the control group (N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills. © The Author(s) 2024.

关键词： argumentative writing collaborative concept map conversational agents digital annotation undergraduate students

来源：评论

学校读者我要写书评

暂无评论

Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

引用

IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

EEG based stress classification by using difference plots of variational modes and machine learning

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Scar-Aware Late Mechanical Activation Detection Network for Optimal Cardiac Resynchronization Therapy Planning

Scar-Aware Late Mechanical Activation Detection Network for ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Jiarui Xing Shuo Wang Amit R. Petal Kenneth C. Bilchick Frederick H. Epstein Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Medicine University of Virginia Health System USA Department of Biomedical Engineering University of Virginia Health System USA Department of Computer Science University of Virginia USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes.

关键词： Image segmentation Accuracy Motion segmentation Medical treatment Magnetic resonance Myocardium Lead Data models Planning Strain

来源：评论

学校读者我要写书评

暂无评论

Comparison of Sequential Feature Selection Performance with Various Dimensional Data to Produce Optimal Classification 8

Comparison of Sequential Feature Selection Performance with ...

引用

8th IEEE Information Technology International Seminar, ITIS 2022

作者： Rahajoe, Ani Dijah Agussalim Setyaningsih, Emy Mandyartha, Eka Prakarsa Akbar, Fawwaz Muttaqin, Faisal Universitas Pembangunan Nasional Veteran Jawa Timur Department of Computer Science Surabaya Indonesia Institut Sains & Teknologi Akprind Department of Computer System Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Feature selection is one part of preprocessing which aims to reduce data dimensions. This study aims to produce optimal performance of the best feature selection method implemented on low- and high-dimensional data. The feature selection methods used are Sequential Forward Selection (SFwS) and Sequential Backward Selection (SBwS) algorithms. Meanwhile, to test the results of the best feature selection algorithm using the classification algorithm Logistic Regression, Linear Regression, k-Nearest Neighbor (kNN). The low dimensional data test results show that the SFS algorithm with the kNN classifier and the Regression group has the best average score. The SFwS algorithm with Logistic Regression has the best average score in the high dimensional data set. The minimal number of feature selections resulted from the SFwS algorithm rather than the SBwF algorithm. Although it should have the same accuracy or number of selected features, in this study, the results were different except for SFS-Linear Regression. The highest average accuracy score for low dimensional data is the Wine dataset of 0.994 (SFS-kNN), and for high dimensional data is the Parkinson's disease dataset of 1 (SFS-LGR). The least number of feature selections obtained from the SFwS-Logistic Regression algorithm is one feature with an accuracy score of 0.8083. The Sequential Backward Selection algorithm generally has a longer running time than the Sequential Forward Selection. © 2022 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Smart Traffic Control system for Dubai: A Simulation Study Using YOLO Algorithms

Smart Traffic Control System for Dubai: A Simulation Study U...

引用

2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Al Mamlook, Rabia Emhamed Zahrawi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Shresth, Sujeet University of Al-Zawiya Industrial Engineering and Engineering Management Libya Trine University Indiana United States British University in Dubai Engineering and Information Technology Dubai United Arab Emirates Yarmouk University Department of Computer Science Information Technology and Computer Science Irbid211633 Jordan Wayne State University Department of Industrial and System Engineering DetroitMI48201 United States

ISBN: (纸本)9781665493765

Dubai's growing population and public transportation have led to an increase in vehicular traffic and associated challenges. To tackle these issues, there is a rising interest in using machine learning (ML)techniques to improve the city's traffic control system. This study aims to explore the potential of ML in enhancing traffic management and creating a sustainable urban environment. A novel approach to traffic management in Dubai that combines AI and ML algorithms has been proposed, with the potential to significantly improve traffic flow and safety. A simulation study on a smart traffic control system that utilizes YOLO algorithms for real-time vehicle detection and counting is presented. The system optimizes traffic light timings and balances traffic load among different roads to reduce congestion and improve traffic flow. The simulation results demonstrate that the system is highly effective in adapting to changing traffic conditions and reducing congestion. The study concludes that the use of ML algorithms such as YOLO has the potential to revolutionize traffic management in urban areas, leading to a more efficient and sustainable transportation system. Further research and development in this area could bring significant benefits to both motorists and the environment. © 2023 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Advanced Data Segmentation and Encryption Approach using Fog Computing

Advanced Data Segmentation and Encryption Approach using Fog...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Varshney, Neeraj Kumar, Pushpendra Sharma, Pradeep Kumar Mishra, Umang Deep Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Scanno System Greater Noida India Hcl Technology UP India Unique Institute of Management & Technology Muradnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

Data security in conventional applications was only available at the application layer. Within the scope of the intended research, the safety of the merchandise is ensured. The deficiencies that are present in the security systems that are already in place have led to the realisation that a new security system is required. Considering that authentication is not present, the likelihood of decryption should be reduced. Establishing a decentralised security system is essential in order to protect against threats coming from a variety of digital networks. The new encryption standard is said to improve multilayer security, according to the claims. The material is split into two halves using this method, which also provides a reliable transmission mechanism. Because of the technology, hackers are unable to create vulnerabilities in the security system. There was a need for security in both the cloud and the edge environment in order to prevent intruders. Increasing the reliability and security of data transmission over a variety of networks was the motivation for the development of the two fish approach. Because of its security mechanism, it has been able to survive assaults from crackers and hackers. The paper that is being presented advises doing research on the safety of edge transmissions that are based on slicing. In order to prevent attackers from launching attacks from several networks, it is necessary to establish decentralised security. Layered security will help increase encryption standards, which is a significant benefit. Over the course of various investigations, computer security edge generators have been the focus of attention. Quite a few of them have already been extensively discussed. For the purpose of developing a reliable method of transmission, the proposed research splits the data into two parts, each of which is further encrypted at a different level. As a result of the development of multilayer encryption, the reliability and safety of data transm

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Critical Factors of Immersive Learning in Higher Education: A systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

引用

10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：