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Coordinated Efficient Buoys Data Collection in Large Complex Coastal Environments using UAVs

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Coordinated Efficient Buoys Data Collection in Large Complex...

Oceans (Conference)

作者： J. Braga F. Balampanis A. Pedro Aguiar J. Sousa I. Maza A. Ollero Department of Electrical and Computer Engineering University of Porto Portugal Robotics Vision and Control Group (GRVC) University of Seville Spain

ISBN: (纸本)9781509064298;9780692946909

Deploying a large number of sensing buoys is a powerful tool for oceanographic, marine biology and climate change research. In this work we address the problem of efficient data collection by a team of Unmanned Aerial Vehicles (UAV) from low-power, small buoys with previously unknown locations in complex coastal or remote oceanic regions with aerial restrictions. To tackle this problem we propose and develop a 3-layers architecture that search for the buoys, enables communication with them and creates a UAV communication network to recover the data. Extensive simulations show that the proposed work both optimizes the searching area coverage path planning for complex regions and the quality-of-service for the data transmission from the buoys to the final destination using UAVs as relay nodes.

关键词： buoys final destination

Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure

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arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

Minimum uncertainty latent variable models for robot recognition of sequential human activities

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Minimum uncertainty latent variable models for robot recogni...

2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Han, Fei Reardon, Christopher Parker, Lynne E. Zhang, Hao Human-Centered Robotics Lab Department of Computer Science Colorado School of Mines GoldenCO80401 United States US Army Research Laboratory AdelphiMD20783 United States Distributed Intelligence Laboratory Department of Electrical Engineering and Computer Science University of Tennessee TN37996 United States

ISBN: (纸本)9781509046331

Recognition of sequential human activities, such as 'sitting down' and 'standing up', is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCRFs), demonstrated satisfactory recognition accuracy, they do not explicitly model the uncertainty in underlying temporal patterns, which can provide valuable information to characterize sequential activities. To address this problem, we introduce a novel Minimum Uncertainty HCRF (MU, or μHCRF). Different from traditional HCRF-based techniques that only utilize the negative log-likelihood of the categories' conditional probability as the loss function, the proposed μ-HCRF also introduces a regularization term to model the underlying temporal pattern of the latent variables. As another theoretical contribution, we provide a derivation to show that the formulated problem has a closed-form solution, and prove that inference of the proposed μHCRF is tractable. Extensive empirical study is performed to evaluate our approach, using four public benchmark datasets. Experimental results have shown that our μHCRFs outperform previous techniques and achieve state-of-the-art performance on human activity recognition, especially on sequential activities. © 2017 IEEE.

关键词： Human robot interaction

Single image super resolution - When model adaptation matters

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arXiv 2017年

作者： Liang, Yudong Timofte, Radu Wang, Jinjun Gong, Yihong Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zürich Switzerland

In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external priors modeling are the key ingredients. The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors. In this paper we propose a novel deep convolutional neural network carefully designed for robustness and efficiency at both learning and testing. Moreover, we propose a couple of model adaptation strategies to the internal contents of the low resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors we achieve 0.1 up to 0.3dB PSNR improvements over best reported results on standard datasets. Our adaptation especially favors images with repetitive structures or under large resolutions. Moreover, it can be combined with other simple techniques, such as back-projection or enhanced prediction, for further improvements. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

Robustness to adversarial examples through an ensemble of specialists

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arXiv 2017年

作者： Abbasi, Mahdieh Gagné, Christian Computer Vision and Systems Laboratory Electrical and Computer Engineering Department Université Laval QuébecQC Canada

Due to the recent breakthroughs achieved by Convolutional Neural Networks (CNNs) for various computer vision tasks (He et al., 2015;Taigman et al., 2014;Karpathy et al., 2014), CNNs are highly regarded technology for inclusion into real-life vision applications. However, CNNs have a high risk of failing due to adversarial examples, which fool them consistently with the addition of small perturbations to natural images, undetectable by the human eyes. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural networks

Wearable Electronics: Self‐Bondable and Stretchable Conductive Composite Fibers with Spatially Controlled Percolated Ag Nanoparticle Networks: Novel Integration Strategy for Wearable Electronics (Adv. Funct. Mater. 49/2020)

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Advanced Functional Materials 2020年第49期30卷 2070321-2070321页

作者： Chaebeen Kwon Duhwan Seong Jeongdae Ha Dongwon Chun Jee‐Hwan Bae Kukro Yoon Minkyu Lee Janghoon Woo Chihyeong Won Seungmin Lee Yongfeng Mei Kyung‐In Jang Donghee Son Taeyoon Lee Nanobio Device Laboratory School of Electrical and Electronic Engineering Yonsei University 50 Yonsei‐ro Seodaemun‐gu Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University 2066 Seobu‐ro Jangan‐gu Suwon‐si Gyeonggi‐do 16419 Republic of Korea Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) 333 Techno Jungang‐daero Hyeonpung‐eup Dalseong‐gun Daegu 42988 Republic of Korea Advanced Analysis Center Korea Institute of Science and Technology (KIST) 5 Hwarang‐ro 14‐gil Seongbuk‐gu Seoul 02792 Republic of Korea Department of Materials Science Fudan University Shanghai 200438 P. R. China

In article number 2005447, Donghee Son, Taeyoon Lee, and co‐workers develop self‐bondable and self‐weavable fibers as novel components for fiber‐based electronic devices. The fibers are both conductive and stretchable, which eliminates the trade‐off associated with the percolation theory. The integration with self‐bondable and self‐weavable interconnects represents a new integration strategy for fiber‐based devices.

关键词： fiber component integration self‐bondable conductive fibers stretchable and flexible interconnects wearable electronics

IRNDT Inspection Via Sparse Principal Component Thermography

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IRNDT Inspection Via Sparse Principal Component Thermography

IEEE Canadian Conference on electrical and computer engineering

作者： Bardia Yousefi Stefano Sfarra Fabrizio Sarasini Xavier P. V. Maldague Department of Electrical and Computer Engineering Computer vision and systems laboratory (CVSL) Laval University Quebec City (Quebec) GIV OA6 Canada Piazzale E. Pontieri University of L'Aquila-Department of Industrial and Information Engineering and Economics (DIIIE) no. 1 I-67100 Monteluco di Roio Roio Poggio-L'Aquila (AQ) Italy Department of Chemical Engineering Materials Environment Sapienza University of Rome Rome I-00184 Italy

ISBN: (纸本)9781538624111

The recent applications in the field of thermography and Infrared Non-Destructive Testing (IRNDT) involved many different research fields, and in most of these applications well-known infrared approaches have been utilized for thermal image enhancement, thermal image segmentation, and particularly defect segmentation in IRNDT. Principal Component Analysis (PCA) or Principal Component Thermography (PCT) is one of these methods that has been countlessly used and it is unequivocally one of the constantly referred approaches in this field. Unfortunately, it suffers from being a linear transformation and besides that, finding appropriate basis through its eigenimage decomposition is the shortcoming. Here, an application of non-linear eigen decomposition using Sparse Principal Component Analysis/Thermography (Sparse-PCA or Sparse-PCT) is addressed for segmentation of defects inherent to two hybrid composites (carbon and flax fiber epoxy prepregs). The results indicate considerable segmentation performance when it is compared to similar approaches.

关键词： sparse principal component analysis/thermography infrared non-destructive testing image segmentation

Stereo image de-fencing using smartphones

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Stereo image de-fencing using smartphones

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sankaraganesh Jonna Sukla Satapathy Rajiv R. Sahay Computational Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Computational Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781509041183

Conventional approaches to image de-fencing have limited themselves to using only image data in adjacent frames of the captured video of an approximately static scene. In this work, we present a method to harness disparity using a stereo pair of fenced images in order to detect fence pixels. Tourists and amateur photographers commonly carry smartphones/phablets which can be used to capture a short video sequence of the fenced scene. We model the formation of the occluded frames in the captured video. Furthermore, we propose an optimization framework to estimate the de-fenced image using the total variation prior to regularize the ill-posed problem.

关键词： Image de-fencing image inpainting stereo disparity map convolutional neural networks total variation Smartphone images Imagery (Psychotherapy) Image Stereo pairs Video sequences stereo imaging Frame image data Videotapes Photographers fencing FRAMES

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

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arXiv 2019年

作者： Kuijf, Hugo J. Biesbroek, J. Matthijs Bresser, Jeroen de Heinen, Rutger Andermatt, Simon Bento, Mariana Berseth, Matt Belyaev, Mikhail Cardoso, M. Jorge Casamitjana, Adrià Collins, D. Louis Dadar, Mahsa Georgiou, Achilleas Ghafoorian, Mohsen Jin, Dakai Khademi, April Knight, Jesse Li, Hongwei Lladó, Xavier Luna, Miguel Mahmood, Qaiser McKinley, Richard Mehrtash, Alireza Ourselin, Sébastien Park, Bo-yong Park, Hyunjin Park, Sang Hyun Pezold, Simon Puybareau, Elodie Rittner, Leticia Sudre, Carole H. Valverde, Sergi Vilaplana, Verónica Wiest, Roland Xu, Yongchao Xu, Ziyue Zeng, Guodong Zhang, Jianguo Zheng, Guoyan Chen, Christopher Flier, Wiesje van der Barkhof, Frederik Viergever, Max A. Biessels, Geert Jan Image Sciences Institute UMC Utrecht Utrecht University Netherlands Brain Center Rudolf Magnus UMC Utrecht Utrecht University Netherlands Department of Radiology UMC Utrecht Department of Radiology LUMC Leiden Netherlands Department of Biomedical Engineering University of Basel Allschwil Switzerland Radiology and Clinical Neuroscience Hotchkiss Brain Institute University of Calgary AB Canada NLP Logix Skolkovo Institute of Science and Technology School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical Image Computing University College London Signal Theory and Communications Department Universitat Politècnica de Catalunya BarcelonaTech Barcelona Spain McGill University Canada Computational Statistics and Machine Learning MSc University College London TomTom Amsterdam Netherlands Department of Radiology and Imaging Science National Institutes of Health United States Ryerson University Canada University of Guelph Canada Sun Yat-sen University University of Dundee Technical University of Munich Research institute of Computer Vision and Robotics University of Girona Spain Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Pakistan Institute of Nuclear Science and Technology Support Center for Advanced Neuroimaging Institute for Diagnostic and Interventional Neuroradiology Inselspital University of Bern Switzerland Electrical and Computer Engineering Department University of British Columbia Vancouver Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston School of Biomedical Engineering and Imaging Sciences King’s College London Suwon Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Republic of France School of Electrical and Computer Engineering University of Campinas SP Brazil School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical I

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://***/).Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner *** participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen *** challenge remains open for future submissions and provides a public platform for method evaluation. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging