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arXiv 2022年

作者： Ta, Kevin Bruggemann, David Brödermann, Tim Sakaridis, Christos Van Gool, Luc The Computer Vision Lab ETH Zurich Zurich8092 Switzerland The Department of Electrical Engineering KU Leuven Leuven3000 Belgium

As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly register lidar laser returns, allowing information-based correlation methods to optimize for the 6-DoF extrinsic calibration between the two sensors. This paper presents the first direct calibration method between event cameras and lidars, removing dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements. © 2022, CC BY.

关键词： Cameras

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Accelerating LULESH using HPX - the C++ Standard Library for Parallelism and Concurrency

Accelerating LULESH using HPX - the C++ Standard Library for...

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2024 Practice and Experience in Advanced Research Computing, PEARC 2024

作者： Singanaboina, Srinivas Yadav Wei, Weile Tsaousis Seiras, Isidoros Syskakis, Panagiotis Richardson, Bradley Cook, Brandon Kaiser, Hartmut Electrical Engineering and Computer Science Louisiana State University Baton RougeLA United States STE||AR Group SunnyvaleCA United States Aristotle University of Thessaloniki Thessaloniki Greece National Energy Research Scientific Computing Center BerkeleyCA United States Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9798400704192

The rapid increase in computer hardware capabilities demands the need for greater parallel efficiency. While the existing CPU parallelization techniques using OpenMP provide adequate performance on parallelizing intensive scientific compute workloads, they lack user flexibility in controlling parallelism, which can enhance performance. In this paper we describe a new port of Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics(LULESH), a widely studied benchmark in the Department of Energy co-design efforts for ExaScale computing, to HPX and HPXMP (a new OpenMP backend of HPX), and an evaluation of the performance on Intel, AMD, ARM, and RISC-V architectures. We explain how we progressively incorporated HPX's parallel features to boost the performance of an HPX-enabled LULESH, including using parallel execution policy, hpx::for_loop, and HPX's fork-join executor. We compare the shared memory parallelism performance of five LULESH versions: NVC++ implementation using C++ algorithms, OpenMP pragma-based implementation, HPX implementation using C++ algorithms, HPX using fork-join executors, and HPXMP. Using HPXMP we observed an average speedup of 1.4x, 1.5x, 1.2x, and 1.3x relative to OpenMP on Intel, AMD, ARM, and RISC-V processors respectively across 3 different workload sizes. We observed a speedup of 1.1x and 2.2x relative to OpenMP on Intel and AMD respectively using Fork Join Executors. These findings suggest that HPXMP can offer a direct enhancement to existing OpenMP codebases across a wide range of architectures, without any modifications to the original source code, whereas HPX's fork-join executors provide finer control of parallelism and increased performance. HPXMP also provides a viable first step on a migration path for OpenMP applications to HPX. © 2024 Owner/Author.

关键词： Benchmarking

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and Control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2023年第4期10卷 1-30页

作者： Mavromatis∗, Ioannis Spyridopoulos, Theodoros Carnelli, Pietro Chin, Woon Hau Khalil, Ahmed Chakravarty, Jennifer Kun, Lucia Cipolina Piechocki, Robert J. Robbins, Colin Cunnington, Daniel Chase, Leigh Chiazor, Lamogha Preston, Chris Rahul Khan, Aftab Digital Catapult London United Kingdom Bristol Research & Innovation Laboratory Toshiba Europe Ltd. Bristol United Kingdom School of Computer Science & Informatics Cardiff University Cardiff United Kingdom Department of Electrical and Electronic Engineering University of Bristol Bristol United Kingdom Nexor Ltd. Nottingham United Kingdom IBM Research Winchester United Kingdom Ltd. Reading United Kingdom

The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper presents an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster research, testing, and evaluation of the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs. © (2023) I. Mavromatis et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://***/licenses/by/4.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Intelligent systems

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Neuronal Modeling Tool Using DynaSim 5

Neuronal Modeling Tool Using DynaSim

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

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Direct Multipath-Based SLAM

arXiv

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arXiv 2024年

作者： Liang, Mingchao Leitinger, Erik Meyer, Florian The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States The Signal Processing and Speech Communication Laboratory Graz University of Technology Graz8010 Austria The Scripps Institution of Oceanography The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States

In future wireless networks, the availability of information on the position of mobile agents and the propagation environment can enable new services and increase the throughput and robustness of communications. Multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 718-752页

作者： Othmane Friha Mohamed Amine Ferrag Lei Shu Leandros Maglaras Xiaochan Wang Networks and Systems Laboratory University of Badji Mokhtar-AnnabaAnnaba 23000Algeria Department of Computer Science Guelma UniversityGulema 24000Algeria College of Engineering Nanjing Agricultural UniversityNanjing 210095China School of Engineering University of LincolnLincoln LN67TSUK School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK Department of Electrical Engineering Nanjing Agricultural UniversityNanjing 210095China

This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart *** begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware *** also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural ***,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural ***,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart ***,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

关键词： Agricultural internet of things(IoT) internet of things(IoT) smart agriculture smart farming sustainable agriculture

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