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VSAN:A new visualization method for super-large-scale academic networks

Frontiers of computer Science 2024年第1期18卷 119-137页

作者： Qi LI Xingli WANG Luoyi FU Xinde CAO Xinbing WANG Jing ZHANG Chenghu ZHOU Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Oceanography Shanghai Jiao Tong UniversityShanghai 200240China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China

As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and *** the booming development of science and technology,the number of papers has been growing *** like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more *** this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between *** order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout *** main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different *** addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic *** our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in t

关键词： academic networks large graph visualization graph layout graph loading

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Efficient Brillouin frequency shift extraction method employing Brillouin gain spectrum fitting in the distributed BOTDA fiber sensor 10

Efficient Brillouin frequency shift extraction method employ...

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10th IEEE International Conference on Photonics, ICP 2024

作者： Hamzah, Abdulwahhab Essa Hamzah, Mustafa Essa Ghadzali, Intan Fatimah Sasila Fadhel, Mahmoud Muhanad Sapiee, Nurfarhana Mohamad Zan, Mohd Saiful Dzulkefly Tanaka, Yosuke Mohamad, Hisham Ali, Sawal Hamid Md Bakar, Ahmad Ashrif A. Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Division of Advanced Electrical and Electronic Engineering Institute of Engineering Tokyo University of Agriculture and Technology Japan Civil & Environmental Engineering Department Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia

ISBN: (纸本)9798350383164

—This paper demonstrates the deployment of quadratic curve fitting (QCF) based on half-peak Brillouin gain spectrum (BGS) for fast and accurate Brillouin frequency shift (BFS) extraction. The method was analysed and compared with three other methods such as Lorentzian curve fitting (LCF), Gaussian curve fitting (GCF) and pseudo-Voigt curve fitting (pVCF). The QCF based on half-peak achieved an accuracy similar to other curve fitting methods. In addition, the QCF has a faster processing time by eight-fold than the others, allowing the system to handle a large amount of data with good performance. The results would provide some general ideas for deploying the QCF algorithm for the system on chip platform with direct detection for fast BFS measurement. ©2024 IEEE.

关键词： System-on-chip

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Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2023年 4卷 2729-2732页

作者： Gong, Xiaowen Huang, Kaibin Chen, Mingzhe Fischione, Carlo Zhang, Jun Choi, Wan Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States The University of Hong Kong Department of Electrical and Electronic Engineering Pokfulam Hong Kong Kth Royal Institute of Technology Division of Networks and System Engineering Stockholm114 28 Sweden University of Miami Department of Electrical and Computer Engineering MiamiFL33146 United States The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Sai Kung Hong Kong Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance.

关键词： Special issues and sections Edge computing Distributed computing Wireless networks Learning systems Machine learning

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Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

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IEEE Communications Letters 2025年第6期29卷 1200-1204页

作者： Chien, Trinh Van Quan, Nguyen Minh Shin, Oh-Soon Nguyen, Van-Dinh Hanoi University of Science and Technology School of Information and Communications Technology Hanoi100000 Viet Nam Soongsil University School of Electronic Engineering Seoul06978 Korea Republic of Center for Environmental Intelligence VinUniversity College of Engineering and Computer Science Vinhomes Ocean Park Hanoi100000 Viet Nam

The integration of unmanned aerial vehicles (UAVs) into wireless communication systems has emerged as a transformative approach, promising cost-efficient connectivity. This paper addresses the optimization of the dynamic time-splitting ratio and flight trajectory for a communication system linking a ground base station to the UAV equipped with backscatter devices (referred to as UB), and from UB to an end user. Given the inherent non-convexity of the problem, we develop two meta-heuristic-based approaches inspired by genetic algorithm and particle swarm optimization to enhance the total achievable rate while reducing computational complexity. Numerical results demonstrate the effectiveness of these meta-heuristic solutions, showcasing significant improvements in the achievable rate and computation time compared to existing benchmarks. © 1997-2012 IEEE.

关键词： Genetic algorithms

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad Department of Mechanical Engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPAUSA Department of Mechanical Engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Electrical and Computer Engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics

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Vision transformer with feature calibration and selective cross-attention for brain tumor classification

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Iran Journal of computer Science 2025年第2期8卷 335-347页

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 Indianapolis IN United States Faculty of Electrical and Electronic Engineering Shahed University Tehran Iran Department of Chemical and Environmental Engineering University of Cincinnati Cincinnati 45221 OH 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). The FCM calibrates the features from different branches to make them more compatible, while The 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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Attention mechanism Brain tumor classification Deep learning Medical imaging analysis Vision transformer

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AC magnetic susceptibility of magnetic nanoparticles measured under DC bias magnetic field

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Journal of the Magnetics Society of Japan 2022年第2期46卷 42-48页

作者： Noguchi, Shoya Trisnanto, Suko Bagus Yamada, Tsutomu Ota, Satoshi Takemura, Yasushi Division of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan Department of Electrical and Electronic Engineering Shizuoka University Hamamatsu432-8561 Japan

The magnetization dynamics of magnetic nanoparticles are essential for the medical applications of hyperthermia and magnetic particle imaging. In this paper, we clarified the AC magnetic susceptibility of superparamagnetic nanoparticles and their frequency dependence. We measured the AC magnetic susceptibility under an applied DC bias magnetic field. The frequency dependence of AC magnetic susceptibility was clearly observed. The experimentally obtained characteristics of the samples with different effective core diameters and dependence on the intensity of the DC bias magnetic field agreed with the theory assuming a simple particle model. The sample comprising effective core diameters with a wide distribution exhibited different characteristics than the samples comprising magnetically fractionated nanoparticles with a narrower core diameter distribution. © 2022, Magnetics Society of Japan. All rights reserved.

关键词： Medical applications

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GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

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Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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Rapid Amplification of Single-Stranded DNA by On-Bead Plasmonic PCR in an Automated Microfluidic System 38

Rapid Amplification of Single-Stranded DNA by On-Bead Plasmo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Anni Bu, Yang Liu, Yuze Lee, Yung Ching Yobas, Levent The Hong Kong University of Science and Technology Division of Bioengineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331508890

This paper presents a novel automated microfluidic system for rapid plasmonic on-beads polymerase chain reaction (PCR). The highly integrated system automates essential PCR workflows, including sample loading, thermal cycling, purification washes, and elution utilizing pneumatic valves. Additionally, gold nanorods are introduced into the PCR system to leverage their efficient photothermal conversion properties to accelerate the PCR reaction. The platform completes 20 PCR cycles within 12 minutes to amplify single-stranded DNA (ssDNA), demonstrating potential for various rapid ssDNA PCR applications, such as aptamer selection and generation. © 2025 IEEE.

关键词： Microfluidics

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