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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Nishat, Humaira Balaji, T.S. Shargunam, S. Sasikala, R. Rani, K. Cvr College of Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai600026 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India

ISBN: (纸本)9798350383348

The data collected by the authorised sensor devices is transferred to the cloud for safe storage. Communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time. © 2024 IEEE.

关键词： GAIT Authentication Internet of Things (IoT) KNN

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird’s-Eye View and Perspective View

arXiv

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arXiv 2024年

作者： Hou, Jiawei Li, Xiaoyan Guan, Wenhao Zhang, Gang Feng, Di Du, Yuheng Xue, Xiangyang Pu, Jian School of Computer Science Fudan University Shanghai China Faculty of Information Technology Beijing University of Technology Beijing100124 China Mogo Auto Intelligence and Telematics Information Technology Co. Ltd China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China

In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird’s-eye view (BEV) semantic segmentation. Recent researchers have extensively explored various aspects of this task, including view transformation techniques, ground-truth label generation, and elaborate network design, aiming to achieve superior performance. However, the inference speed, crucial for running on an autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is proposed. By carefully analyzing the network effect and latency from four parts, including the input image resolution, image backbone, view transformation, and occupancy prediction head, it is found that the occupancy prediction head holds considerable potential for accelerating the model while keeping its accuracy. Targeted at improving this component, the time-consuming 3D convolution network is replaced with a novel residual-like architecture, where features are mainly digested by a lightweight 2D BEV convolution network and compensated by integrating the 3D voxel features interpolated from the original image features. Experiments on the Occ3D-nuScenes benchmark demonstrate that our FastOcc achieves state-of-the-art results with a fast inference speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Study of Enhancing Computational Thinking Skills through STEAM Robotics Activities 2

A Study of Enhancing Computational Thinking Skills through S...

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2nd International Annual Meeting on STEM Education, IAMSTEM 2019

作者： Phadung, Muneeroh Yokkhun, Atcharaporn Persoh, Sulaiman Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Information Technology Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Computer Science Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand

STEAM robotics activities are designed to be an enhancement on students' computational thinking skills, which are claimed to be essential for the 21st century. The aims of this study were to examine the learning outcomes of secondary students' computational thinking skills through the use of STEAM robotics activities, and to investigate the students' satisfaction after the implementation of STEAM robotics activities. A quasi-experimental research design was deployed in this study. The research participants were 37 eleventh-grade students of Private Islamic Schools in Yala province, Thailand. A quiz with pre- and post-test and satisfaction questionnaires were employed as data. Descriptive statistics - including mean, standard deviation and a pair sample t-test - was used to analyse the collected data. The findings were that students' learning outcomes indicated a significant improvement in their computational skills after the use of STEAM robotics activities as compared with before, with the statistical level of significance of 0.05. In response to the second research question, the overall satisfaction of the students after the use of such STEAM robotics activities was high (mean=4.01;SD=0.54). © Published under licence by IOP Publishing Ltd.

关键词： Robotics

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WiFi-based Positioning System with k-means Clustering and Outlier Removal: Evidence from Multiple Datasets 23

WiFi-based Positioning System with k-means Clustering and Ou...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Pham-Hai, Dang Duong-Bao, Ninh He, Jing Nguyen Thi, Luong Lee, Seon-Woo Nguyen-Huu, Khanh Department of Electronics and Telecommunications Dalat University Dalat Viet Nam College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798400708916

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms. © 2023 ACM.

关键词： Indoor positioning systems

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Error analysis of transformer hot spot temperature measurement

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Distributed Generation and Alternative Energy Journal 2021年第4期36卷 403-424页

作者： Zhao, Zhengang Yang, Zhengyu Wang, Yuyuan Liang, Ke Jin, Nengsi Shi, Kaiqiang Li, Chuan Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Yunnan Key Laboratory of Computer Technology Applications Yunnan Kunming China Yunnan Institute of Measuring and Testing Technology Yunnan Kunming China

According to the national standard GB/T 1094.7-2008, the method of hot spot measurement of oil-immersed transformer is used to place several temperature sensors inside the gasket within the predicted hot spot position to measure the temperature of winding transformer. The highest temperature measured is regarded as the hot spot temperature of transformer. Since the winding and gasket are bad conductors of heat, there exists certain temperature difference between the gasket and the hot spot temperature of the winding. This temperature difference is not mentioned in the national standard GB/T 1094.7-2008, which is bound to affect the accuracy of the transformer hot spot temperature *** order to ensure safe operation of transformer, the thermal environment of temperature measuring point is analyzed and the discrete equation of boundary node is established. The parameters are set according to the heat transfer mode of the oil-immersed transformer and the temperature characteristics of each heat transfer node is analyzed. Gauss-Seidel Iteration method is used to calculate the theoretical value of the measuring point of the oil-immersed transformer and the heat transfer model of the measuring point is established for further analysis. The experimental platform of the oil-immersed transformer simulator is established according to the method described in the national standard and used to measure the hot spot temperature and winding surface temperature. The results show that when the winding temperature is 77◦C, the heat transfer model of the temperature measuring point is 74.7◦C and the experimental temperature of the temperature measuring point is 74.9◦C. The relative error between theoretical calculation temperature and experimental temperature is 0.27%. As the temperature of the experiment increases, the temperature difference between the temperature point and the winding temperature gradually increases, and the maximum absolute error is 2.1◦C. © 2021 River Pu

关键词： Temperature measurement

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MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yubiao Yue Jun Xue Haihuang Liang Zhenzhang Li Yufeng Wang School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model’s ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba’s parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba.

关键词： Source coding Transfer learning Self-supervised learning Feature extraction Skin Complexity theory Lesions Speech processing Viruses (medical) Context modeling

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A Generative Adversarial Network for infrared and visible image fusion using adaptive dense generator and Markovian discriminator

引用

Optik 2023年第1期288卷

作者： Liu, Gang Liu, Yang Tang, Lili Bavirisetti, Durga Prasad Wang, Xiao School of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Existing infrared and visible image fusion algorithms have problems with reduced saliency for infrared targets and a lack of background texture information. To address this problem, a generative adversarial network for image fusion based on an attention mechanism is proposed in this study. This network consists of a generator, a discriminator, and an adaptive decision block. The generator employs a DenseNet-based cascade approach, which substantially lowers the information loss during the convolution process. A Markovian discriminator, unlike global discriminators, is embedded in the network. It divides images into patches, causing the network to focus on local regions and distinguishing the generated images as a whole. In addition, an adaptive decision block is included in the model to build an intensity dynamic mapping mechanism. To guide the computation for a certain content loss, a weighted map is created based on the intensity information of infrared images. Unlike the other loss functions, the weighted parameters of each component of the loss function are adaptively evaluated. To demonstrate the effectiveness of the proposed method, qualitative and quantitative experiments on TNO and RoadScene datasets are carried out. The experimental findings suggest that the proposed technique retains more significant characteristics in source images and produces fused images with rich texture details. Furthermore, the system noise resistance of each approach is examined. Under the condition of different noise, the fusion results of our approach still exhibit less distortion and a higher peak signal-to-noise ratio when compared to other state-of-the-art fusion methods. © 2023 Elsevier GmbH

关键词： Generative adversarial networks

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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Biomedical Engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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A Comparison Study of FC-RDVM and RIWM Router Placement Methods for WMNs: Performance Evaluation Results by WMN-PSOHC Simulation System Considering Chi-Square Distribution and Different Instances 1

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Sakamoto, Shinji Barolli, Admir Liu, Yi Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer Science National Institute of Technology Oita College 1666 Maki Oita870-0152 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Kajino-Machi Koganei-Shi184-8584 Japan

ISBN: (数字)9783031290565

ISBN: (纸本)9783031290558

In this work, we deal with the node placement problem in Wireless Mesh Networks (WMNs). We present a hybrid intelligent simulation system called WMN-PSOHC, which combines Particle Swarm Optimization (PSO) and Hill Climbing (HC). We implement in WMN-PSOHC system the Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) and Random Inertia Weight Method (RIWM) router replacement methods. By using WMN-PSOHC system, we carry out simulations the access the performance of these two methods considering Chi-Square distribution of mesh clients and two different instances. The simulation results show that FC-RDVM performs better then RIWM in the considered scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

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