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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Jin, Yufei Zhu, Xingquan Florida Atlantic University Department of Electrical Engineering and Computer Science Boca RatonFL33431 United States

ISBN: (纸本)9798331506681

Graph neural networks (GNN) have been commonly used for learning and classifying objects with correlated relationships. To date, many GNN architectures exist, but majority of them only work well on shallow networks due to the oversmoothing phenomenon, where node features become similar to each other, as the layer increases. In this paper, we point out that the key to create an informative deep GNN is to have an adaptive feature updating rate control for each node, where the updating rate should take each node's locality into consideration through shared trainable weight parameters. Accordingly, we advocate a new graph rhythm modeling as a generalized mechanism to the Dirichlet energy based approaches. Instead of merely modeling difference between nodes, like Dirichlet energy based approach does, graph rhythm focuses on omni-directional relationship mapping between each node and its neighbors. Such a mechanism provides a more general ways of capturing patterns between nodes (i.e. graph rhythm) for effective graph neural network learning. Experiments and comparisons, demonstrate the performance gain and show that GRN can help create GNNs with deep layers, without suffering from performance deterioration or having better performance than shallow networks. © 2024 IEEE.

关键词： Graph neural networks

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A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Srichandan, Suresh Kumar Jena, Sudarson Majhi, Santosh Kumar Mishra, Kaushik Veer Suredra Sai University of Technology Department of Computer Science and Engineering Odisha Sambalpur Burla India Sambalpur University Institute of Information Technology Sambalpur Univesity Department of Computer Science and Engineering Odisha India Guru Ghasidash Vishwavidyalaya Department of Computer Science and Information Technology C. G. Birlaspur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350379525

The Internet of Things (loT) is rapidly advancing, but its limited resources are often supplemented by integrating fog computing to address these constraints. Despite this, fog devices encounter challenges such as heterogeneity, widespread distribution, dynamic nature, and resource limitations. To tackle these issues, a robust task scheduling strategy is crucial for efficiently utilizing fog resources and enhancing Quality of Service (QoS). Moreover, tasks generated are dynamic in nature with disparate requirements such as deadline, cost, priority, etc. Fog resources could be utilized effectively with proper mapping of tasks based on ingress traffic on each node to meet the requirements. This research formulates the task mapping problem (TMP) with the goal of minimizing cost while improving QoS by reducing response times and deadline violations for loT tasks. It uses SVM (Support Vector Machine) to predict fog node traffic and categorizes nodes into peak-traffic and low-traffic groups. The Quantum-inspired PSO-based TM Algorithm (QPSO) has been implemented to map the tasks on these nodes. Simulation results reveal that the proposed method offers significant improvements, achieving up to 19.66% better response times, 30% cost reduction, and a 26% deadline violation rate i.e., an increase in meeting task deadlines compared to other approaches. © 2024 IEEE.

关键词： Cost reduction

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AVIG: A Real-Time Visual Inspection for Guava Grading System Using computer Vision and XGBoost 23

AVIG: A Real-Time Visual Inspection for Guava Grading System...

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8th International Conference on Sustainable information engineering and Technology, SIET 2023

作者： Setyawan, Raden Arief Perdana, Rizal Setya Harilegawa, Made Wena Balantimuhe, Alan Stevrie Basuki, Achmad Department of Electrical Engineering Faculty of Engineering Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Directorate of Information Technology Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

The conventional approach to manual inspection can be described by its considerable time and labor demands and the unavoidable occurrence of human error when faced with the inspection of a large number of products. Therefore, the application of an automated visual inspection system is frequently employed. The camera(s) can easily capture visual features for automatic visual inspection. However, it is essential to note that there are limitations when using this method to estimate the size-based grading system. The volume measurement is an essential feature in the field of automatic image analysis for fruit and vegetable grading, specifically guava. In this study, we propose a novel image analysis approach that utilizes a dual-view camera system to enhance the accuracy of volume approximation, particularly in scenarios involving non-perfect spherical objects. Hence, this research presents the training process of a comprehensive model for estimating the volume of objects with irregular shapes, named AVIG (Automatic Visual Inspection for Grading). The model utilizes the ellipsoid approximation technique, a dual-view camera, and XGBoost algorithm. The results from the accuracy comparison demonstrate that XGBoost shows the highest degree of accuracy, specifically 86%, thereby outperforming the performance of other algorithms. The comprehensive experiment conducted in this study has shown that the proposed architecture, AVIG, exhibits superior performance compared to previous research in terms of automatic evaluation of moving objects. © 2023 ACM.

关键词： computer vision

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Alzheimer's Disease Stage Classification using Blood Data 2

Alzheimer's Disease Stage Classification using Blood Data

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rasheed, Asif Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead University Department of Computer Science Thunder Bay Canada Western University Department of Computer Science London Canada Idaho State University Department of Electrical and Computer Engineering Pocatello United States

ISBN: (纸本)9798331529529

In this work, we aim to evaluate the performance of Machine Learning models in the classification of Alzheimer's patients into disease stages using two feature selection methods proposed in our previous work. The first method identifies relevant features by ranking them according to their dependency on the diagnosis, which is measured using metrics such as Mutual information, Symmetric Uncertainty and Cramer's V. The second method filters relevant features by applying a threshold to the Euclidean distance between the class means for each feature. Three new approaches are proposed to accommodate multiple classes in the Euclidean distance-based feature selection method. These approaches involve scoring each feature using the minimum, maximum and average of the Euclidean distances between the class means. Using two datasets, we extensively tested all possible combinations of relevant features identified using the two methods, ensuring minimal correlation among features in each panel. The panels achieved accuracies as high as 75.17% and 85.46% on the first and second datasets, respectively. The performances achieved in this work demonstrate the feasibility of using blood-based biomarkers in Alzheimer's disease stage classification and could help in developing accurate low-cost and non-invasive early detection tools that could aid in Alzheimer's disease diagnosis. © 2024 IEEE.

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Economic Optimal Operation of Integrated Rural Single Household Energy Systems 3

Economic Optimal Operation of Integrated Rural Single Househ...

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3rd International Conference on Energy engineering and Power Systems, EEPS 2023

作者： Wang, Zhaoyang Yang, Dechang Dehghaniang, Payman China Agricultural University College of Information and Electrical Engineering Beijing China George Washington University Department of Electrical and Computer Engineering WA United States

ISBN: (纸本)9798350313857

People in rural areas have increasingly high requirements for quality of life and gradually increasing demand for energy. Traditional rural household energy systems often rely on the power grid and natural gas. In order to reduce economic costs and improve energy use efficiency, this paper proposes an economically optimized operation scheme for a single rural household integrated energy system. Firstly, constructing the integrated rural household energy system and modeling the components of the system. Secondly, taking the example of a rural household in the European region, we analyze the energy use on a typical day in summer and a typical day in winter. With the objective functions of purchasing energy costs and operating costs, the CPLEX solver in MATLAB is called to establish an optimization model for the economic operation of the rural household integrated energy system. Through the analysis, it is verified that the construction of the integrated rural household energy system is economical. © 2023 IEEE.

关键词： Operating costs

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Coexistence of Distributed Sensing and Ultra-simple Coherent RoF based Front-haul for Radio Access Network

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Journal of Lightwave Technology 2025年第12期43卷 5669-5675页

作者： Liu, Maoqi Wang, Jingchuan Liu, Chen Yu, Changyuan Lu, Chao Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Wuhan430074 China The Hong Kong Polytechnic University Photonics Research Institute Department of Electrical and Electronic Engineering Hong Kong

The radio access network requires high-speed transmission and multifunctionality to support the increasing demand for advanced communication services. Radio-over-fiber (RoF) technology fulfills these requirements by enabling the delivery of millimeter-wave (MMW) band wireless signals via optical fiber front-haul. This paper presents an ultra-simple RoF system that achieves the coexistence of distributed sensing and telecommunication. A commercially available coherent modulator achieves signal modulation and remote up-conversion by the beat between the residual carrier and the modulated radio signal. The linear-frequency modulated carrier enables distributed sensing while simultaneously serving as the beat tone of RoF communication, showcasing the potential for integrated sensing and communication (ISAC) applications in next-generation radio access networks. We demonstrate the sensing and transmission performance over a 10-km fiber link and a 1-m wireless MMW channel. The system achieves a distributed sensing detection sensitivity of 28 pΕ/√ Hz and supports 1.6 GBaud 16QAM radio transmission in the Ka-band of 28 GHz. © 1983-2012 IEEE.

关键词： Radio access networks

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Single-Image Driven 3D Viewpoint Training Data Augmentation for Effective Label Recognition 27th

Single-Image Driven 3D Viewpoint Training Data Augmentation ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Yueh-Cheng Chen, Hsin-Yi Hung, Cheng-Jui Chuang, Jen-Hui Hwang, Jenq-Neng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering University of Washington Seattle United States

ISBN: (纸本)9783031781247

Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint transformation technique initially tailored for label recognition. This technique can be used not only for data augmentation by generating synthetic data from any perspective but also transform photos taken from any angle into a frontal view, thereby reducing the complexity of the recognition task. Given the extensive use of wine-related applications with over 20 million users and the continuous publication of wine label datasets, we decided to focus this study on wine labels. This method enhances deep learning model performance by generating visually realistic training samples from a single real-world label image, overcoming the challenges posed by the intricate combinations of text and logos. Unlike classical Generative Adversarial Network (GAN) methods, which fall short in synthesizing such intricate content combinations and require a large amount of training data to become effective, our proposed solution leverages time-tested computer vision and image processing strategies. By using just a single monocular wine label image, we can expand our training dataset, thereby broadening the range of training samples for deep learning applications. This innovative approach to data augmentation circumvents the constraints of limited training resources. We then utilize the augmented training images through the Vision Transformer (ViT) architecture, performing one-shot recognition of existing wine labels in the training classes or future newly collected wine labels unavailable in the training. Experimental results show a significant increase in recognition accuracy over conventional 2D data augmentation techniques, indicating the potential for broader application in various labeling scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wine

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Evolving Semantic Communication with Generative Modelling 35

Evolving Semantic Communication with Generative Modelling

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Tang, Shunpu Yang, Qianqian Gunduz, Deniz Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Imperial College London Department of Electrical and Electronic Engineering London United Kingdom

ISBN: (纸本)9798350362244

Learning-based semantic communication (SemCom) has emerged as a promising solution for the upcoming 6G networks. In this paper, we explore an evolving SemCom system for image transmission, which can continuously adapt and enhance its transmission efficiency by exploiting knowledge accumulated during previous transmissions. Specifically, we propose a novel channel-aware semantic encoder that utilizes a pretrained generative model to extract channel-correlated latent variables consisting of several semantic vectors from the input images, which can be directly transmitted over a noisy channel without further channel coding. Moreover, we introduce a dynamic code construction mechanism that dynamically updates the codebook with transmitted semantic vectors to eliminate the need to transmit similar codes in subsequent transmissions, thus further reducing the communication overhead. Simulation results highlight the evolving performance of the proposed system in terms of transmission efficiency, achieving superior perceptual quality with an average bandwidth compression ratio (BCR) of 1 / 192 for a sequence of 100 test images compared to DeepJSCC and InverseJSCC. Code used in this paper is available at https://***/recusant7/GAN_SeCom. © 2024 IEEE.

关键词： Bandwidth compression

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The Analysis of Device-to-Device (D2D) Communication System Operating in 5G Underlaying Cellular Networks on Multiple Bands over Nakagami-m Fading Channels

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SN computer science 2025年第5期6卷 1-15页

作者： Shatnawi, Razan A. Khodeir, Mahmoud A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Al-Zuraiqi, Eyad T. Qasaymeh, Mahmoud M. Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Computer Engineering Department School of Engineering The University of Jordan Amman 11942 Jordan Department of Telecommunications Engineering Hijjawi Faculty for Engineering Technology Yarmouk University P.O. Box 566 Irbid 21163 Jordan Computer Engineering and Communication Department Tafila Technical University Tafila 66110 Jordan

In contrast to traditional cellular connection, device-to-device (D2D) communication is a direct connection amidst adjacent mobile users that does not pass through the base station (BS) and does not rely on network infrastructure to meet low power requirements and high data rate. Cellular networks and D2D connections were combined in underlay mode to address power usage concerns. In underlay D2D communication, licensed frequency bands are utilized by both D2D and cellular connections;due to resource sharing, these bands may overlap, which lowers service quality but boosts spectral efficiency. Most existing research exclusively discuss energy efficiency (EE) in the context of single cells. This article has taken into account the entire cellular network with D2D connections on various bands, and stochastic geometric theory has been used in order to obtain closed form expressions for the probability of successful transmission (STP), the average sum rate (ASR), and EE for D2D connection underlaid cellular networks over Nakagami-m fading channels. Results of simulations are used to validate analytical conclusions. Simulation results demonstrate that higher STP, higher ASR, and lower EE all result in enhanced system performance. In addition, as the shape parameter’s value m (Nakagami-m order) increases, the EE, ASR, and STP of D2D users improve, which enhances system performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Device-to-device communication Energy efficiency Nakagami-m fading channel Stochastic geometry

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Model-Free Formation Control of Autonomous Underwater Vehicles:A Broad Learning-Based Solution

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1325-1328页

作者： Wenqiang Cao Jing Yan Xian Yang Xiaoyuan Luo Xinping Guan the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Institute of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,We develop a broad learning-based algorithm to enforce the formation control of *** with the deep learning(DL)based formation solutions,our solution employs the broad learning system(BLS)to remodel the lea... 详细信息

关键词： process system learning

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