检索结果-内蒙古大学图书馆

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

学校读者我要写书评

暂无评论

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： Cloud computing Privacy Smart cities Pipelines Semantics Robot sensing systems Software

A toolkit to generate social navigation datasets

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Goa India Robotics and Artificial Vision Laboratory University of Extremadura Spain Dept. of Computer Engineering Pune Institute of Computer Technology India Dept. of Computer Science College of Engineering and Physical Sciences Aston University United Kingdom

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. Copyright © 2020, The Authors. All rights reserved.

关键词： Navigation

Stonefish: An Advanced Open-Source Simulation Tool Designed for Marine robotics, With a ROS Interface

学校读者我要写书评

暂无评论

Stonefish: An Advanced Open-Source Simulation Tool Designed ...

OCEANS - Europe

作者： Patryk Cieślak Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona Girona Spain

The marine robotics community is lacking a high quality simulator for doing scientific research, especially when it comes to testing control and vision algorithms in realistic underwater intervention tasks. All of the solutions used today are either outdated or try to combine different software tools, which often results in bad performance, stability issues and lack of important features. This paper presents a new software tool, focused on, but not limited to, simulation of intervention autonomous underwater vehicles (I-AUV). It delivers advanced hydrodynamics based on actual geometry, simulation of underwater sensors and actuators, as well as realistic rendering of underwater environment and ocean surface. It consists of a library written in C++ and a Robot Operating System (ROS) package.

关键词： Atmospheric modeling Libraries Robot sensing systems Actuators Rendering (computer graphics)

Learning Context-Based Non-local Entropy Modeling for Image Compression

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Timofte, Radu Zhang, David Zuo, Wangmeng Zhang, Kai Li, Mu School of Science and Engineering Chinese University of Hong Kong ShenzhenGuangdong518172 China Computer Vision Laboratory ETH Zurich Zurich Switzerland School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Shenzhen Research Institute of Big Data Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes plays a vital role in the performance. However, existing deep learning based entropy modeling methods generally assume the latent codes are statistically independent or depend on some side information or local context, which fails to take the global similarity within the context into account and thus hinder the accurate entropy estimation. To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context. Specifically, we first introduce the proxy similarity functions and spatial masks to handle the missing reference problem in context modeling. Then, we combine the local and the global context via a non-local attention block and employ it in masked convolutional networks for entropy modeling. The entropy model is further adopted as the rate loss in a joint rate-distortion optimization to guide the training of the analysis transform and the synthesis transform network in transforming coding framework. Considering that the width of the transforms is essential in training low distortion models, we finally produce an U-Net block in the transforms to increase the width with manageable memory consumption and time complexity. Experiments on Kodak and Tecnick datasets demonstrate the superiority of the proposed context-based non-local attention block in entropy modeling and the U-Net block in low distortion compression against the existing image compression standards and recent deep image compression models. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

Research on Neural Networks Integration for Object Classification in Video Analysis Systems

学校读者我要写书评

暂无评论

Research on Neural Networks Integration for Object Classific...

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Ivan Fomin Vladislav Burin Aleksandr Bakhshiev Computer vision lab Russian State Scientific Center for Robotics and Technical Cybernetics Saint-Petersburg Russia Institute of Metallurgy Mechanical Engineering and Transport Peter the Great St.Petersburg Polytechnic University Saint-Petersburg Russia

ISBN: (数字)9781728145907

ISBN: (纸本)9781728145914

Object recognition with the help of outdoor video surveillance cameras is an important task in the context of ensuring the security at enterprises, public places and even private premises. There have long existed systems that allow detecting moving objects in the image sequence from a video surveillance system. Such a system is partially considered in this research. It detects moving objects using a background model, which has certain problems. Due to this some objects are missed or detected falsely. We propose to combine the moving objects detection results with the classification, using a deep neural network. This will allow determining whether a detected object belongs to a certain class, sorting out false detections, discarding the unnecessary ones (sometimes individual classes are unwanted), to divide detected people into the employees in the uniform and all others, etc. The authors perform a network training in the Keras developer-friendly environment that provides for quick building, changing and training of network architectures. The performance of the Keras integration into a video analysis system, using direct Python script execution techniques, is between 6 and 52 ms, while the precision is between 59.1% and 97.2% for different architectures. The integration, made by freezing a selected network architecture with weights, is selected after testing. After that, frozen architecture can be imported into video analysis using the TensorFlow interface for C++. The performance of such type of integration is between 3 and 49 ms. The precision is between 63.4% and 97.8% for different architectures.

关键词：

Attention-driven dynamic graph convolutional network for multi-label image recognition

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ye, Jin He, Junjun Peng, Xiaojiang Wu, Wenhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition. However, constructing a graph by counting the label co-occurrence possibilities of the training data may degrade model generalizability, especially when there exist occasional co-occurrence objects in test images. Our goal is to eliminate such bias and enhance the robustness of the learnt features. To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic Graph Convolutional Network (D-GCN) to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM). Extensive experiments on public multi-label benchmarks demonstrate the effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current state-of-the-art methods with a clear margin. All codes can be found at https://***/Yejin0111/ADD-GCN. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

Interconnected robotic platforms inform deep-sea ecological restoration trends

学校读者我要写书评

暂无评论

Marine Pollution Bulletin 2025年 219卷

作者： Ivan Masmitja Narcís Palomeras Daniel M. Toma Nixon Bahamon Matias Carandell José A. García del Arco Damianos Chatzievangelou Marta Real Antoni Sánchez-Márquez Gerard Batet Pau Vial Francisco Bonin-Font Caterina Muntaner Natàlia Hurtós Juan M. López Alex R. Ripoll Guillermo de Arcas Jordi Grinyó Spartacus Gomariz Kakani Katija Joan B. Company Institut de Ciències del Mar (ICM) CSIC Barcelona 08003 Spain Computer vision and Robotics Institute Universitat de Girona (UdG) Girona 17003 Spain SARTI-MAR Research group Universitat Politècnica de Catalunya (UPC) Barcelona Tech. Barcelona 08034 Spain Systems Robotics and Vision Group University of the Balearic Islands (UIB) Palma de Mallorca 07122 Spain IQUA Robotics Girona 17003 Spain Universidad Politécnica de Madrid (UPM) Grupo de Investigación en Instrumentación y Acústica Aplicada Madrid 28031 Spain Research and Development Bioinspiration Lab Monterey Bay Aquarium Research Institute (MBARI) Moss Landing CA 95062 USA Institut Català de Recerca per a la Governança del Mar (ICATMAR) Barcelona 08003 Spain

The implementation of Marine Protected Areas (MPAs) has increased in the last few decades, creating a deep impact on ocean governance and local fishery communities. Despite their effectiveness being actively debated, initial results show that this spatial management could promote the recovery of depleted marine communities and species, and ecosystem services such as increased fishing profits, especially in nearby areas. This could potentially maintain the economic levels of local and regional under-pressure economies while improving the overall marine biodiversity. However, the lack of ecological monitoring systems for long-lasting, non-invasive, and multiparametric measurements in near-real-time is a roadblock to national-level monitoring programs in those areas. Here we present a set of technological solutions, including robotics, biologging, and machine learning approaches to monitor MPAs, developed and validated in different deep-sea field experiments. The outcomes of these trials have yielded insights into the fine-scale behavioral ecology of different deep-sea species and the recovery dynamics of overexploited seafloors. With these tools, it is possible to monitor restored areas via ecological indicators, that are key to informing and empowering policymakers, conservation biologists, and fishery ecology experts, propelling an effective approach to the conservation and governance of marine ecosystems.

关键词： Marine protected areas Conservation Restoration robotics Machine learning Underwater technology Autonomous vehicles Acoustics Ecosystem services Ocean monitoring

Underwater image super-resolution using deep residual multipliers

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Islam, Md Jahidul Enan, Sadman Sakib Luo, Peigen Sattar, Junaed Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota Twin Cities United States

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of ‘high’ (640 × 480) and ‘low’ (80 × 60, 160 × 120, and 320 × 240) resolution. USR-248 contains over 7K paired instances in each set of data for supervised training of 2×, 4×, or 8× SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models’ performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

Sparus II AUV as a Sensor Suite for Underwater Archaeology: Falconera Cave Experiments

学校读者我要写书评

暂无评论

Sparus II AUV as a Sensor Suite for Underwater Archaeology: ...

Symposium on Autonomous Underwater Vehicle Technology (AUV)

作者： Guillem Vallicrosa Manuel J. Fumas Florian Huber Pere Ridao Computer Vision and Robotics Institute (VICOROB) Universitat de Girona Girona Spain International School of Doctorate in Marine Studies (EIDEMAR) Universidad de Cádiz Cádiz Spain Scientific Diving Operations (Submaris) Kiel Germany

ISBN: (数字)9781728187570

ISBN: (纸本)9781728187587

The present work tries to improve the topographical and prospective methodology applied to underwater archaeology, as well as the different technical tools used in the study of submerged caves, using as a experimental site one of the most contaminated and dangerous submerged caves in Europe called Falconera Cave (Sitges, Barcelona). The results have shown that the application of traditional procedures together with the use of the Sparus II Autonomous Underwater Vehicle (AUV) equipped with a multibeam echosounder represent the best technical solution for georeferencing in this submerged archaeological contexts.

关键词： Meters Surfaces Robot sensing systems Water pollution Trajectory Sea measurements Europe