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International Conference on Communication Technology (ICCT)

作者： Chunyi Ma Jiajie Xu Jianhua Yang Mustafa A. Kishk School of Automation Northwestern Polytechnical University Xi’an China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Electronic Engineering Maynooth University Maynooth Ireland

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

In order to satisfy the rapidly growing demand for data traffic from massive users, this study investigates a space-ground LEO satellite-assisted edge caching network framework and analyses the downlink transmission performance of this edge caching network. The aim is to enhance the quality of service provided by the edge caching network to users through the utilisation of LEO satellites. We use stochastic geometry to analyse the downlink performance. The spatial locations of the LEO satellites are modeled as Binomial Point Process (BPP) distribution deployed at a specific space altitude, and the locations of the terrestrial cloud servers (CSs) and mobile users (MUs) are modelled as independent Poisson Point Process (PPP), respectively. We establish the association strategy of typical MUs to LEO satellites (SATs) and CSs, based on which we derive the coverage probability analytical expression and average delay numerical solution for this network system. Finally, we verify the correctness of the numerical result through Monte Carlo simulation and study the network performance as a function of key constellation design parameters. We also analyse the impact of LEO SAT constellation parameters on the coverage capability and the average delay of MUs in this edge caching network. The simulation and analysis results provide constructive insights into the network design.

关键词： Geometry Performance evaluation Satellites Low earth orbit satellites Stochastic processes Quality of service Downlink Delays Numerical models Servers

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Classical approaches and new deep learning trends to assist in accurately and efficiently diagnosing ear disease from otoscopic images

Classical approaches and new deep learning trends to assist ...

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European Workshop on Visual Information Processing (EUVIP)

作者： Dhruv Chetan Jobanputra Mohammed Bennamoun Farid Boussaid Lian Xu Jafri Kuthubutheen Computer Science and Software Engineering The University of Western Australia Perth Australia Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia Division of Surgery Medical School The University of Western Australia Perth Australia

Rural communities in Australia have limited access to Ear, Nose and Throat (ENT) specialists, resulting in a lack of expertise to provide a diagnosis of complex and chronic ear diseases. This literature review examines previous attempts at creating a computer-aided tool to accurately diagnose ear disease and gaps in the literature. A systematic search was conducted to identify relevant papers and the latest best trends in technology. Four papers showed significant results in ear disease detection with deep learning models providing the best performance. Some studies using larger datasets consisting of endoscopic images obtained accuracies of over 90%. No adequate model was found that used otoscopic images with a sensitivity of over 90%. Endoscopic images provide better quality images, making it unclear how the models would perform on otoscopic images. Advanced techniques such as Transformers have not yet been tested in ear disease detection and could help improve model accuracy.

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Differential Linear Matrix Inequalities in Optimal and Robust Sampled-Data Filtering

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IFAC-PapersOnLine 2023年第2期56卷 9806-9811页

作者： Henrique W.R. Pereira Gabriela W. Gabriel André M. de Oliveira José C. Geromel Electronic Engineering Division ITA São José dos Campos SP Brazil Institute of Science and Technology UNIFESP São José dos Campos SP Brazil School of Electrical and Computer Engineering UNICAMP Campinas SP Brazil

This paper tackles the design of a robust filter for sampled-data linear time-invariant systems subject to parameter uncertainties. After the optimal solution is obtained, the two major classes of parameter uncertainty are considered, namely, convex bounded and norm bounded. For each of them, the filter associated to the minimum H2 guaranteed performance is determined. The design conditions are expressed through differential linear matrix inequalities (DLMIs), a mathematical device well adapted to cope with sampled-data systems because it naturally avoids matrix exponential and similar mappings. In this framework, the optimal nominal filter is determined, and it is shown that it exhibits a time-invariant observer-based structure. Illustrative examples are presented and discussed in order to put in clear evidence the theoretical results.

关键词： Sampled-data filtering robust estimation convex optimization observer design

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More on Maximally Permissive Similarity Control of Discrete Event Systems

arXiv

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arXiv 2024年

作者： Wang, Yu Zhu, Zhaohui van Glabbeek, Rob Takai, Shigemasa Zhang, Jinjin Tan, Lixing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Informatics University of Edinburgh United Kingdom Division of Electrical Electronic and Infocommunications Engineering Osaka University Japan School of Computer Science Nanjing Audit University China College of Information Engineering Taizhou University China School of Computer Science and Engineering University of New South Wales Australia

Takai proposed a method for constructing a maximally permissive supervisor for the similarity control problem (IEEE Transactions on Automatic Control, 66(7):3197–3204, 2021). This paper points out that this construction does not (necessarily) work when the specification is not image-finite. Inspired by Takai’s construction, the notion of a (saturated) (G, R)-automaton is introduced and metatheorems concerning (maximally permissive) supervisors for the similarity control problem are provided in terms of this notion. As an application of these metatheorems, the flaws in Takai’s work are corrected. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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Zeroth-Order and Vortex Bessel Beams from Single-Mode Fibers with 3D Printed Photonic Structures

Zeroth-Order and Vortex Bessel Beams from Single-Mode Fibers...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Innem V.A.K Reddy Andrea Bertoncini Carlo Liberale Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering University at Buffalo NY USA Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

ISBN: (纸本)9798350345995

Diffraction-free propagation and self-healing characteristics [1] of Bessel beams have generated significant interest in the scientific community. Zeroth-order Bessel beams (ZOBBs) have maximum intensity at the center and found many applications, including bio-imaging material processing, optical trapping, bio-imaging, and quantum communication. On the other hand, higher-order (vortex) Bessel beams (HOBBs) present a helical wavefront, so they have a node due to a phase singularity and carry orbital angular momentum (OAM). Over the past years, Bessel beams have been generated by using different approaches - which could be different for ZOBBs and HOBBs - namely by Fourier transforming a narrow circular beam with a lens, by using axicons or with holographic beam shaping techniques.

关键词： Beam shaping Fourier transforms Materials processing Optical trapping Optical vortices Single mode fibers

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WiFo: Wireless Foundation Model for Channel Prediction

arXiv

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arXiv 2024年

作者： Liu, Boxun Gao, Shijian Liu, Xuanyu Cheng, Xiang Yang, Liuqing State Key Laboratory of Photonics and Communications School of Electronics Peking University Beijing100871 China Guangzhou511400 China Department of Electronic and Computer Engineering Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

Channel prediction permits to acquire channel state information (CSI) without signaling overhead. However, almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency (STF) wireless foundation model (WiFo) to address time-frequency channel prediction tasks in a unified manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder (MAE)based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160K CSI samples for pre-training. Simulations validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art (SOTA) zero-shot generalization performance via comparisons with other full-shot baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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GCN-Enhanced Multi-Agent MAC Protocol for Vehicular Communications 22

GCN-Enhanced Multi-Agent MAC Protocol for Vehicular Communic...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Mengfei Wang, Wei Feng, Shijie Fu, Tiange Wang, Yan Zhang, Hui State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Sichuan Jiuzhou Electronic Group Co. Ltd. Mianyang621000 China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331527471

In the vehicular ad-hoc network (VANET) environment, the constant changes in traffic systems due to the real-time mobility of vehicles pose a challenge in developing communication strategies that can swiftly adapt to dynamic topological variations. Traditional protocols such as CSMA/CD offer dynamic access to shared media, but their efficiency is compromised by CSMA/CA’s collision avoidance strategy, which reduces channel use due to its backoff algorithm. This paper introduces a novel Graph Convolutional Network-based Multi-Agent Reinforcement Learning MAC protocol (GMMAC) for vehicular networks. Unlike the Neuro-DCF protocol, GMMAC incorporates local vehicular data directly, enabling more context-aware learning. Compared to other GNN-based MARL approaches like GraphComm, GMMAC better handles real-time changes and interactions among vehicles. Experimental results show that GMMAC significantly improves successful transmission numbers and reduces collisions, outperforming traditional CSMA/CA methods and other MARL approaches without GCN integration. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration

arXiv

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arXiv 2024年

作者： Khaniki, Mohammad Ali Labbaf Mirzaeibonehkhater, Marzieh Manthouri, Mohammad Hasani, Elham Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Indiana University Purdue University United States Faculty of Electrical and Electronic Engineering Shahed university Tehran Iran Department of Chemical and Environmental Engineering University of Cincinnati CincinnatiOH45221 United States

Brain tumor classification is a challenging task in medical image analysis, with significant implications for patient diagnosis and treatment. The objective of this paper is to propose a novel approach to brain tumor classification using a Vision Transformer (ViT) with a novel cross-attention mechanism. Our approach leverages the strengths of transformers in modeling long-range dependencies and multi-scale feature fusion. We introduce two new mechanisms to improve the performance of the cross-attention fusion module: Feature Calibration Mechanism (FCM) and Selective Cross-Attention (SCA). FCM calibrates the features from different branches to make them more compatible, while SCA selectively attends to the most informative features. Our experimental results demonstrate that the proposed approach outperforms other state-of-the-art methods, including Convolutional Neural Networks (CNN), ViT, and Cross-attention ViT, achieving an accuracy of 98.93% and an F1-score of 98.83%. Furthermore, the addition of Stochastic Depth mechanism improves the accuracy to 99.24% and the F1-score to 99.23%. The proposed FCM and SCA mechanisms can be easily integrated into other ViT architectures, making them a promising direction for future research in medical image analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A GTFS data acquisition and processing framework and its application to train delay prediction

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International Journal of Transportation Science and Technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory Data fusion

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Tenacity prediction of polyester materials based on an ensemble soft measurement model

Tenacity prediction of polyester materials based on an ensem...

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2024 China Automation Congress, CAC 2024

作者： Duan, Jiaxu Wang, Jun Tao, Youji Ma, Zhiping Wu, Xin Hu, Zhou State Key Laboratory of Environmental Adaptability for Industrial Products China National Electric Apparatus Research Institute Co. Ltd Guangzhou China Kinte Materials Science and Technology Co. Ltd Guangzhou China Department of Computer Science Hunan University of Technology and Business Changsha China School of Electronic Information and Electrical Engineering Yangtze University Jingzhou China

ISBN: (纸本)9798350368604

In the development process of polyester powder coating materials, it has great guiding significance for the development of new materials as the polyester material properties can be predicted through the given monomer structure and formula. In order to meet the requirements of rapid development of polyester materials, this paper establishes a prediction method based on an ensemble soft measurement model (ESM) for one key property-tenacity in prompting the acceleration of development cycle of polyester products. The processed data employed to train and test the model were collected from a local material company, and it covers over 100 kinds of polymerized resins. Firstly, the molecular expressions of monomers are converted to SMILES codes and further translated to ECFP fingerprints. Then, the principal component analysis (PCA) method is adopted to extract the elements with higher importance in each fingerprint. Finally, the ensemble model that consists of 4 machine learning models was established to make the multi-classification decision in tenacity prediction. The effectiveness of the prediction model is evaluated by comparing the predicted and the practical tensile test results. The results demonstrate that this approach achieves better performance in the material property prediction as well as generalization capability. © 2024 IEEE.

关键词： Tensile testing

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