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2023 International Conference on Smart Applications, communications and Networking, SmartNets 2023

作者： Da Costa Rocha, Jesse De Souza, Marcela Alves Cardoso, Evelin Helena Silva Vijaykumar, Nandamudi De Araujo, Jasmine Priscyla Leite Frances, Renato Lisboa Graduate Program in Electrical Engineering Belém Para Brazil Information and Communication Technology Center Marabá Para Brazil Department of Computer Science Capitão Poço Para Brazil Coordination of Applied Research and Technological Development São Paulo Brazil

ISBN: (纸本)9798350302523

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics. © 2023 IEEE.

关键词： Students

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Application of Pose Estimation on Webcam for Fall Risk Assessment 8

Application of Pose Estimation on Webcam for Fall Risk Asses...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Uttama, Surapong Chumpoo, Patsakorn Rueangsirarak, Worasak Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand School of Information Computer Science Program Technology Mae Fah Luang University Chiang Rai Thailand School of Information Technology Computer and Communication Engineering for Capacity Building Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9798350396546

Fall risk assessment is an effective and simple measure to evaluate the balance of people especially the elderly who are likely to have balance disorder. Lose of body balance increases a fall risk which could lead to severe damage to aging persons. In this paper, we propose to apply pose estimation technique based on PoseNet method to detect body joints from webcam's video for two types of fall risk assessment: Five Times Sit to Stand Test (FTSTS) and 30-Second Chair Stand Test (30CST). The experiments were performed with 17 volunteers concurrently with the measures of a healthcare expert. The results revealed that our proposed technique corresponds well with the measure of the expert evaluating by a Pearson correlation coefficient which equals to 0.903 and 0.980 for FTSTS and 30CST respectively. © 2023 IEEE.

关键词： Risk assessment

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SynHING: Synthetic Heterogeneous information Network Generation for Graph Learning and Explanation

arXiv

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arXiv 2024年

作者： Hong, Ming-Yi Huang, Yi-Hsiang Lin, Shao-En Teng, You-Chen Wang, Chih-Yu Lin, Che Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for robust baselines and expansive graph datasets is accentuated, particularly in the context of Heterogeneous information Networks (HIN). Addressing this, we introduce SynHING, a novel framework for Synthetic Heterogeneous information Network Generation aimed at enhancing graph learning and explanation. SynHING systematically identifies major motifs in a target HIN and employs a bottom-up generation process with intra-cluster and inter-cluster merge modules. This process, supplemented by post-pruning techniques, ensures the synthetic HIN closely mirrors the original graph’s structural and statistical properties. Crucially, SynHING provides ground-truth motifs for evaluating GNN explainer models, setting a new standard for explainable, synthetic HIN generation and contributing to the advancement of interpretable machine learning in complex networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and communication technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning

arXiv

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arXiv 2024年

作者： Masuya, Nozomu Sato, Hiroshi Yamane, Koki Kusume, Takuya Sakaino, Sho Tsuji, Toshiaki Master's Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Doctoral Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Department of Intelligent Interaction Technologies Institute of Systems and Information Engineering University of Tsukuba Japan Graduate School of Science and Engineering Saitama University Saitama Japan

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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A Modified Radiopropagation Multipath Model for Constant Refractivity Gradient Profiles

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2023年第2期22卷 298-312页

作者： Parada, D. Rego, C.G. Guevara, D. Navarro, A. Ramos, G.L. Oliveira, R. Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 MG Belo Horizonte Brazil Departament of Electronics Engineering Universidade Federal de Minas Gerais MG Belo HorizonteCEP 31270-901 Brazil Departament of Electricity and Electronics Universidad Francisco de Paula Santander Norte de Santander CúcutaCEP 540003 Colombia Departament of Information and Communication Technologies Universidad ICESI Cali Valle del CaucaCEP 760031 Colombia Departament of Telecommunications and Mechatronics Engineering Federal University of São João del-Rei MG Ouro BrancoCEP 36415-000 Brazil

This paper presents a radiopropagation algorithm based on a Ray Tracing (RT) technique that combines a modified multipath model for constant refractivity gradient profiles and the Uniform Theory of Diffraction (UTD). A novel formulation is proposed by the authors for calculation and ground-reflection analysis of ray paths depending on atmospheric refractivity. The algorithm introduced herein was evaluated in a mixed scenario and in two more realistic case studies, under conditions of constant refractivity gradient and lossy terrain profiles. Pathloss results are obtained and compared with Parabolic Equation (PE) numerical solution results at 2.0 GHz, 3.5 GHz and 5.4 GHz. In such conditions, the modified radiopropagation multipath algorithm with atmospheric refractivity introduced herein showed satisfactory results. © 2023 SBMO/SBMag.

关键词： Refraction

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Denoising CNN Based Channel Estimation for Vehicular OTFS communication System

Denoising CNN Based Channel Estimation for Vehicular OTFS Co...

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International Conference on Advanced communication Technology (ICACT)

作者： Bangwei He Zhiquan Bai Yuanyuan Ma Hao Xu Abeer Mohamed Yingchao Yang KyungSup Kwak Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao China Graduate School of Information Technology and Telecommunications INHA University Incheon Korea

The orthogonal time-frequency space (OTFS) technique can convert the double dispersive channel in the time-frequency (TF) domain into a time-invariant channel in the delayed-Doppler (DD) domain through a series of two dimensional transformations, which shows promising applications in high-speed vehicular communication scenarios. As a key part in OTFS system, conventional pilot-based channel estimation schemes have obvious drawbacks, such as low accuracy and poor robustness to mobility. In this paper, we propose a denoising convolutional neural network (DCNN) based channel estimation scheme in OTFS system for high-speed vehicular communications by introducing hybrid dilated convolution (HDC) and residual paths into convolutional neural network (CNN). Simulation results show that the DCNN method can achieve fast DD domain channel estimation with lower complexity compared with the conventional schemes. Meanwhile, it is robust to different vehicle speeds while satisfying high accuracy in channel estimation.

关键词： Time-frequency analysis communication systems Simulation Noise reduction Channel estimation Feature extraction Robustness

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Assessing the Performance of Machine Learning Algorithms for Water Level Prediction in the Chao Phraya River and its Tributaries: A Focus on Low and High Water Levels

SSRN

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SSRN 2024年

作者： Priyasiri, Wilmat D.S.M. Rittima, Areeya Kraisangka, Jidapa Sawangphol, Wudhichart Phankamolsil, Yutthana Talaluxmana, Yutthana Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Faculty of Information and Communication Technology Mahidol University Thailand Environmental Engineering and Disaster Management Program Mahidol University Kanchanaburi Campus Thailand Department of Water Resources Engineering Faculty of Engineering Kasetsart University Thailand

High precision of hydrological prediction is crucial for real–time operation of flood and drought risk mitigation and strategic planning. This study assessed the predictive performances of three Machine Learning (ML) algorithms;XGBoost, Random Forest (RF), and Multi–Layer Neural Networks (MLNNs) for water level prediction with a focus on low and high water levels. The one–day and one–week water level prediction models for six key gauged stations along the Chao Phraya River and its major tributaries were accordingly developed. Selecting the combination of input features was carried out and structured in the prediction model at all gauged stations based on the physical river–reservoir system using past water level, rainfall, controlled reservoir outflow, and upstream discharges with different travel times. The statistical evaluation indicated that both XGBoost and RF, the tree–based ML models, with rainfall input robustly outperformed than MLNNs, as it strongly achieved higher R2 from 0.957 to 0.999 for model training and from 0.743 to 0.995 for model testing and lower MAE, MSE, and RMSE values for all prediction scenarios. Among these three ML algorithms applied for daily prediction, RF demonstrated the superior performance for low water level prediction exhibiting the smallest percentage error of overestimating lying between +0.0088% and +0.9380%. XGBoost, RF, and MLNNs, demonstrated acceptable performance level for high water level prediction as it shows small percentage error of both overestimating and underestimating lying between –2.2696% and +1.1587%. Additionally, it can capture the entire testing dataset with high precision than weekly model. © 2024, The Authors. All rights reserved.

关键词： Rain

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Enhanced Deep Residual Shrinkage Network Based Channel Estimation in RIS communication System

Enhanced Deep Residual Shrinkage Network Based Channel Estim...

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International Conference on Advanced communication Technology (ICACT)

作者： Yuanyuan Ma Zhiquan Bai Bangwei He Jinqiu Zhao Abeer Mohamed Ke Pang KyungSup Kwak Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao China Graduate School of Information Technology and Telecommunications INHA University Incheon Korea

Reconfigurable intelligent surface (RIS) composed of a large number of passive components can significantly save hardware cost and energy. However, its channel estimation is challenging since RIS commonly operates as a passive reflecting device and cannot acquire the channel state information (CSI) independently. In this paper, a data-driven approach for achieving highly accurate channel estimation in RIS assisted multi-user system is proposed. First, the channel estimation problem is modeled as a residual noise cancellation problem, and then the channel matrix with noise is obtained by a scaled least square (SLS) channel estimation. Finally, an enhanced deep residual shrinkage network (EDRSN) is designed to reduce the noise and further improve the accuracy of channel estimation.

关键词： Costs communication systems Simulation Channel estimation Feature extraction Noise cancellation Hardware

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