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Intelligent Technology and Embedded Systems (ICITES), International Conference on

作者： Xinran Zhou Huayue Lu College of Computer and Network Security Chengdu University of Technology Chengdu China Department of Computer Science and Engineering University of Connecticut Storrs CT USA

Today’s artificial intelligence technology has made great progress and in many fields involved in artificial intelligence, such as medical imaging, logistics, manufacturing, and other fields, one of the most basic tasks is segmentation. Image segmentation is a more difficult segment in segmentation, and its existing problems are: insufficient labeled data leads to weakened performance, and it is difficult to process on the device. Recently, a hyperdimensional-computing-based unsupervised segmentation approach has been proposed, which can process images on-device. However, there are still some problems, like latency. In this work, we have made some improvements based on previous work and propose ImSegHDC having much lower latency with only a little segmentation performance drop.

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EEG-based epileptic seizure pattern decoding using vision transformer

EEG-based epileptic seizure pattern decoding using vision tr...

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International Conference on Innovations in Information Technology

作者： Abdelhadi Hireche Rafat Damseh Parikshat Sirpal Abdelkader Nasreddine Belkacem Department of Computer and Network Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates School of Electrical and Computer Engineering Gallogly College of Engineering University of Oklahoma United States of America

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders.

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Hybrid Digital-Analog Semantic Communications

arXiv

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arXiv 2024年

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. College of Information Science and Technology Jinan University Guangzhou China Department of Electronic Engineering Tsinghua University Beijing China State Key Laboratory of Space Network and Communications Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Digital and analog semantic communications (Sem- Com) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of digital-analog allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDADeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Joint Movement and User Association of UAV-BS for Indoor User Service: A Multi-Agent Deep Reinforcement Learning Approach 22

Joint Movement and User Association of UAV-BS for Indoor Use...

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22nd IEEE Consumer Communications and networking Conference, CCNC 2025

作者： Kim, Tae-Yoon Ahn, Yujeong Lee, Jaeyeol Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Space Electronics and Information Engineering Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798331508050

This paper investigates the joint optimization of movement and user association for multiple UAV base stations (UAV-BSs) to provide emergency services to indoor users. In this paper, we propose a multi-agent deep Q-network (MADQN) reinforcement learning algorithm to address this problem. Moreover, we apply a self-attention mechanism to achieve the objective more effectively. Simulation results demonstrate that the proposed algorithm outperforms conventional algorithms, such as random action and MADQN without a self-attention mechanism. © 2025 IEEE.

关键词： Deep reinforcement learning

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Development of Smart Mask System Integrated with Alert Detection and Vital-sign Measurement

Development of Smart Mask System Integrated with Alert Detec...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wei-Lun Ting Chun-Chieh Hsiao Ren-Guey Lee Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan R.O.C.

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This research focuses on enhancing worker safety in environments with high levels of TVOC gases, such as toluene, a neurotoxin. Traditional cartridge replacement methods, based on fixed intervals, lack precision and pose risks. We propose a novel approach using the SGP30 sensor to monitor cartridge effectiveness, aligned with PEL-TWA regulations. Our system triggers alerts when sensor readings exceed 10 ppm for toluene, 35 ppm for carbon monoxide, and 5000 ppm for carbon dioxide. Additionally, we incorporate physiological monitoring via Photoplethysmography (PPG) using the AFE4404 sensor to assess Heart Rate (HR) and Heart Rate Variability (HRV), alongside respiration monitoring with the D6F-P0010AM2 airflow sensor. Data from these sensors is transmitted via low-power Bluetooth to a mobile APP, enabling real-time monitoring of the wearer's condition. In case of cartridge failure or abnormal physiological readings, the APP triggers vibrations on the mask and automatically sends an SOS to the employer's server via UDP protocol, ensuring immediate intervention. This system aims to enhance occupational safety by reducing the risk of accidents and safeguarding worker well-being.

关键词： Vibrations Protocols Occupational safety Real-time systems Safety Mobile applications Servers Biomedical monitoring Heart rate variability Monitoring

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Optimization Satellite Power Control in Integrated Non-Terrestrial networks Using Multi-Agent Deep Reinforcement Learning 22

Optimization Satellite Power Control in Integrated Non-Terre...

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22nd IEEE Consumer Communications and networking Conference, CCNC 2025

作者： Lee, Jaeyeol Lee, Won-Jae Kim, Tae-Yoon Moon, Taehan Kim, Jae-Hyun Ajou University Department of Space Electronics and Information Engineering Suwon16499 Korea Republic of Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798331508050

High altitude platform station (HAPS) and low Earth orbit (LEO) satellites that provide services to the ground in the line of sight (LoS) environment are gaining attention as next-generation communication networks. Additionally, integrating HAPS and satellite networks is being researched to provide high-quality communication services to terrestrial environments. However, a critical issue in integrated networks is the increased interference, which leads to degraded signal quality for users and a higher outage probability when the same frequency is used. In this paper, we propose a method to reduce outage probability for users being served by the HAPS by adjusting the transmission power of the satellite using deep reinforcement learning (DRL). This paper uses a multi-agent deep Q-network (MADQN) and an attention-based A-MADQN, which combines MADQN with an attention mechanism. By employing reinforcement learning, the optimal transmission power can be determined. The simulations illustrate confirmed a reduction in user outage probability compared to the conventional satellite transmission power for each cell. © 2025 IEEE.

关键词： Deep reinforcement learning

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Development of Gamification and Live Coding-Based Programming Learning Platform to Foster Learning Motivation of Vocational Students

Development of Gamification and Live Coding-Based Programmin...

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Electrical and Information Technology (IEIT), International Conference on

作者： Wahyu Nur Hidayat Wiji Dwi Prasetyo Ayu Najmatus Zahiroh Esa Pramudheva Maydi Syahri Mochammad Mu’iz Afdloly Rahajeng Kartika Sari Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Departement of Computer Network Engineering and Telecommunications SMK Negeri 10 Malang Malang Indonesia

The current utilization of e-learning has not been able to increase participation and learning experience and motivate students to achieve educational goals. This research focuses on developing gamification-based learning media in Object-Oriented Programming (OOP) subjects to increase student learning motivation, determine the feasibility level of learning media, and determine the increase in student learning motivation. The method of the research is design thinking It is used to identify and better understand users in the use of applications, find problems faced by users, and can explore new ideas in the creation of appropriate application features in accordance with user needs. The results of expert media validation from the teacher, lecturer, and programmer amounted to 89.78% in the good category, for the material validation results of 91.33% in the very good category. Media use was implemented by 23 students of class XI RPL SMKN 12 Malang. The user trial results showed a percentage of 83.48% with good criteria. The student learning motivation test results showed a percentage of 81.26% with very high criteria. This shows the effect of gamification implementation in fostering student learning motivation. Gamification-based media has been tested for feasibility by media experts, material experts, and students. Therefore, the media is feasible and can be used as a learning support media in object-oriented programming subjects in Vocational High School.

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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Anovel HEOMGA Approach for Class Imbalance Problem in the Application of Customer Churn Prediction

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SN computer Science 2021年第6期2卷 1-12页

作者： AlShourbaji, Ibrahim Helian, Na Sun, Yi Alhameed, Mohammed School of Computer Science University of Hertfordshire Hatfield United Kingdom Department of Computer and Network Engineering Jazan University Jazan Saudi Arabia Department of Computer Science Jazan University Jazan Saudi Arabia

Making class balance is essential when learning from highly skewed datasets;otherwise, a learner may classify all instances to a negative class, resulting in a high false-negative rate. As a result, a precise balancing strategy is required. Many researchers have investigated class imbalance using Machine Learning (ML) methods due to their powerful generalization performance and interpreting capabilities, comparing with random sampling techniques, to handle the problem of class imbalance in the preprocessing phase to facilitate learning process and improve performance results of learners. In this research, an effective method called HEOMGA is presented by combining Heterogeneous Euclidean-Overlap Metric (HEOM) and Genetic Algorithm (GA) for oversampling minority class. The HEOM is employed to define a fitness function for the GA. To assess the performance of the proposed HEOMGA method, three benchmark datasets from UCI repository in the domain of customer churn prediction are examined using three different ML learners and evaluated with three performance metrics. The experiment results show the effectiveness of the proposed method compared to some popular oversample methods, such as SMOTE, ADASYN, G SMOTE, and Gaussian oversampling methods. The HEOMGA method significantly outperformed the other oversampling methods in terms of recall, G mean, and AUC when the Wilcoxon signed-rank test is used. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Class imbalance problem Classification Genetic algorithm HEOM Oversampling

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