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arXiv 2025年

作者： Pandit, Manya Magadum, Triveni Mittal, Harshit Kushwaha, Omkar University School of Management Studies Guru Gobind Singh Indraprastha University Delhi Dwarka110078 India Computer Science Department KLE Technological University Karnataka Hubballi580031 India Center for Energy and Environment School of Advanced Sciences KLE Technological University Karnataka Hubballi580031 India Pro H2Vis Solutions KLE Technological University Karnataka Huballi580031 India University School of Chemical Technology Guru Gobind Singh Indraprastha University Delhi Dwarka110078 India Energy Consortium ICAR Indian Institute of Technology Chennai Madras600036 India Chemical Engineering Department Indian Institute of Technology Chennai Madras600036 India

The research examines the challenges revolving around young people’s social movements, activism regarding sustainability, as well as the accompanying social media aspect, and how social media impacts environmental action. This study focuses on the environmental craze on social media platforms and its impact on young activists aged 16-25. With the advancement of social media, new avenues have opened for participation in sustainability issues, especially for the marginalized, as information moved through transnational networks at lightning speed. Along with specific Formative Visual Storytelling methods, the young leaders of the movement deploy hashtags and other online tools to capture the attention of their peers and decision makers. Challenges persist with "clicktivism," fatigue from the internet, and site limitations. This article contributes to insights on emerging forms of civic activism by explaining how digital natives adapt technology to reframe green activism. The research suggests that effective digital environmental movements integrate online and offline action, make it simple for individuals to get involved, and promote tolerance to algorithmic modifications and climate care among participants. © 2025, CC BY.

关键词： Tweets

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Adaptive green traffic signal controlling using vehicular communication

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Frontiers of Information Technology & Electronic engineering 2017年第3期18卷 373-393页

作者： Erfan SHAGHAGHI Mohammad Reza JABBARPOUR Rafidah MD NOOR Hwasoo YEO Jason J.JUNG Department of Computer Systems and Technology University of Malaya Department of Computer Engineering Islamic Azad UniversityNorth Tehran Branch Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology Department of Computer Engineering Chung-Ang University

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integral component of intelligent transportation systems(ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems(TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses:(1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and(2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service(Qo S). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communications is used. The V2 V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2 I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network Qo S criteria.

关键词： Vehicular ad hoc network(VANET) Intelligent transportation systems(ITSs) Clustering Adaptive traffic signal control Traffic controller Fuel consumption

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Simultaneous Hydrogen Generation and Exciplex Stimulated Emission in Photobasic Carbon Dots

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Angewandte Chemie 2023年第33期135卷

作者： Dr. Jiawen Fang Prof. Dr. Yiou Wang Mariam Kurashvili Dr. Sebastian Rieger Dr. Wiktor Kasprzyk Qingli Wang Prof. Dr. Jacek K. Stolarczyk Prof. Dr. Jochen Feldmann Dr. Tushar Debnath Chair for Photonics and Optoelectronics Department of Physics Nano-Institute Munich Ludwig-Maximilians-Universität München Königinstr. 10 80539 Munich Germany Advanced Research Institute for Multidisciplinary Sciences MOE Key Laboratory of Cluster Science & School of Chemistry and Chemical Engineering Beijing Institute of Technology No. 5 South Zhongguancun Street Haidian District Beijing 100081 China Department of Biotechnology and Physical Chemistry Faculty of Chemical Engineering and Technology Cracow University of Technology Warszawska 24 31-155 Kraków Poland Smoluchowski Institute of Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 30-348 Krakow Poland Centre for Nanotechnology Indian Institute of Technology Guwahati Guwahati Assam 781039 India

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut N. Ali Angela Amphawan Alaa Fareed Aras Al-dawoodi Awab Noori School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Al-Qalam University College Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University 47500 Petaling Jaya Selangor Malaysia. School of Computing University Utara Malaysia Malaysia. Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed.

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Uav and lidar image registration: A surf-based approach for ground control points selection 4

Uav and lidar image registration: A surf-based approach for ...

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4th ISPRS Geospatial Week 2019

作者： Kalantar, B. Ueda, N. Al-Najjar, H.A.H. Moayedi, H. Halin, A.A. Mansor, S. RIKEN Center for Advanced Intelligence Project Goal-Oriented Technology Research Group Disaster Resilience Science Team Tokyo103-0027 Japan Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Dept. of Multimedia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor43400 Malaysia Dept. of Civil Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400 Malaysia

Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results. © Authors 2019.

关键词： Unmanned aerial vehicles (UAV)

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SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System

SmartPC: Hierarchical Pace Control in Real-Time Federated Le...

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Real-Time Systems Symposium (RTSS)

作者： Li Li Haoyi Xiong Zhishan Guo Jun Wang Cheng-Zhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Big Data Lab (BDL) Baidu Inc. Beijing China Department of Electrical and Computer Engineering University of Central Florida Orlando FL School of Computer Science McGill University Montr??al Canada Department of Computer and Information Science University of Macau Macau China

ISBN: (数字)9781728164632

ISBN: (纸本)9781728164649

Federated Learning is a technique for learning AI models through the collaboration of a large number of resource-constrained mobile devices, while preserving data privacy. Instead of aggregating the training data from devices, Federated Learning uses multiple rounds of parameter aggregation to train a model, wherein the participating devices are coordinated to incrementally update a shared model with their own parameters locally learned. To efficiently deploy Federated Learning system over mobile devices, several critical issues including real-timeliness and energy efficiency should be well addressed. This paper proposes SmartPC, a hierarchical online pace control framework for Federated Learning that balances the training time and model accuracy in an energy-efficient manner. SmartPC consists of two layers of pace control: global and local. Prior to every training round, the global controller first oversees the status (e.g., connectivity, availability, and energy/resource remained) of every participating device, then selects qualified devices and assigns them a well-estimated virtual deadline for task completion. Within such virtual deadline, a statistically significant proportion (e.g., 60%) of the devices are expected to complete one round of their local training and model updates, while the overall progress of multi-round training procedure is kept up adaptively. On each device, a local pace controller then dynamically adjusts device settings such as CPU frequency so that the learning task is able to meet the deadline with the least amount of energy consumption. We performed extensive experiments to evaluate SmartPC on both Android smartphones and simulation platforms using well-known datasets. The experiment results show that SmartPC reduces up to 32:8% energy consumption on mobile devices and achieves a speedup of 2.27 in training time without model accuracy degradation.

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Creating and manipulating a Laughlin-type ν = 1/3 fractional quantum Hall state on a quantum computer with linear depth circuits

arXiv

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arXiv 2020年

作者： Rahmani, Armin Sung, Kevin J. Putterman, Harald Roushan, Pedram Ghaemi, Pouyan Jiang, Zhang Department of Physics and Astronomy and Advanced Materials Science and Engineering Center Western Washington University BellinghamWA98225 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Google AI Quantum Santa BarbaraCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Physics Department City College of the City University of New York New YorkNY10031 United States Graduate Center of the City University of New York New YorkNY10016 United States

Here we present an efficient quantum algorithm to generate an equivalent many-body state to Laughlin’s ν = 1/3 fractional quantum Hall state on a digitized quantum computer. Our algorithm only uses quantum gates acting on neighboring qubits in a quasi one-dimensional setting, and its circuit depth is linear in the number of qubits, i.e., the number of Landau orbitals in the second quantized picture. We identify correlation functions that serve as signatures of the Laughlin state and discuss how to obtain them on a quantum computer. We also discuss a generalization of the algorithm for creating quasiparticles in the Laughlin state. This paves the way for several important studies, including quantum simulation of non-equilibrium dynamics and braiding of quasiparticles in quantum Hall states. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

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Building a quantum engineering undergraduate program

arXiv

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arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： engineering education

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Fine-grained event categorization with heterogeneous graph convolutional networks

arXiv

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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