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Triangle extension: Efficient localizability detection in wireless sensor networks

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arXiv 2018年

作者： Wu, Hejun Ding, Ao Liu, Weiwei Li, Lvzhou Yang, Zheng Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China Horizon Robotics Beijing China School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability *** Codes 68T40 Copyright © 2018, The Authors. All rights reserved.

关键词： Sensor nodes

Haptic fMRI: A novel five DOF haptic interface for multi-axis motor neuroscience experiments 7

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Haptic fMRI: A novel five DOF haptic interface for multi-axi...

7th IEEE World Haptics Conference, WHC 2017

作者： Menon, Samir Soviche, Amaury Mithrakumar, Jananan Subbarao, Alok Ganti, Hari Khatib, Oussama Stanford Robotics Laboratory Department of Computer Science Stanford University StanfordCA94305 United States Robotics and Autonomous Systems Department Ecole Polytechnique Federale de Lausanne Route Cantonale Lausanne1015 Switzerland

ISBN: (纸本)9781509014255

We present a novel electromagnetically actuated Haptic fMRI Interface with five degrees-of-freedom (DOF), HFI-5. The interface uses two three DOF devices connected with a gimbal in a closed kinematic chain to achieve three-axis translation and two-axis rotation. To highlight the device's design, we develop a taxonomy of similar devices and demonstrate why HFI-5's design excels. We use a cross-correlation based novel analysis method to demonstrate that HFI-5 is transparent by showing that it does not affect unconstrained subject motions. This is due to its low friction ( © 2017 IEEE.

关键词： Haptic interfaces

Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

An approach to the bio-inspired control of self-reconfigurable robots 1

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Bie, Dongyang Gutiérrez-Naranjo, Miguel A. Zhao, Jie Zhu, Yanhe State Key Laboratory of Robotics and System Harbin Institute of Technology HarbinHeilongjiang China Department of Computer Science and Artificial Intelligence University of Seville Seville Spain

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

Self-reconfigurable robots are robots built by modules which can move in relationship to each other. This ability of changing its physical form provides the robots a high level of adaptability and robustness. Given an initial configuration and a goal configuration of the robot, the problem of self-regulation consists on finding a sequence of module moves that will reconfigure the robot from the initial configuration to the goal configuration. In this paper, we use a bio-inspired method for studying this problem which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. A promising 3D software simulation and a 2D hardware experiment are also presented. © Springer Nature Singapore Pte Ltd 2017.

关键词： Modular robots

Pose estimation of periacetabular osteotomy fragments with intraoperative x-ray navigation

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arXiv 2019年

作者： Grupp, Robert B. Hegeman, Rachel A. Murphy, Ryan J. Alexander, Clayton P. Otake, Yoshito McArthur, Benjamin A. Armand, Mehran Taylor, Russell H. Department of Computer Science Johns Hopkins University BaltimoreMD United States Research and Exploratory Development Department Johns Hopkins University Applied Physics Laboratory LaurelMD United States Research and Exploratory Development Department Johns Hopkins University Applied Physics Laboratory Auris Surgical Robotics LaurelMD United States Department of Orthopaedic Surgery Johns Hopkins Medicine BaltimoreMD United States Nara Japan Washington Orthopedics and Sports Medicine Chevy ChaseMD United States Dell Medical School University of Texas AustinTX United States Texas Orthopedics AustinTX United States Research and Exploratory Development Department Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States

Objective: State of the art navigation systems for pelvic osteotomies use optical systems with external fiducials. We propose the use of X-Ray navigation for pose estimation of periacetabular fragments without fiducials. Methods: A 2D/3D registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and 6 cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan is refined to more accurately match the intraoperative shape. Results: In simulation, average fragment pose errors were 1.3°/1.7 mm when the planned fragment matched the intraoperative fragment, 2.2°/2.1 mm when the plan was not updated to match the true shape, and 1.9°/2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2°/2.2 mm, 3.8°/2.5 mm, and 3.5°/2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 2° for all fragment shapes in simulation and cadaver experiments. Conclusion: The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability. Significance: Human interpretation of fragment Copyright © 2019, The Authors. All rights reserved.

关键词： Pipelines

The limits and potentials of deep learning for robotics

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arXiv 2018年

作者： Sünderhauf, Niko Brock, Oliver Scheirer, Walter Hadsell, Raia Fox, Dieter Leitner, Jürgen Upcroft, Ben Abbeel, Pieter Burgard, Wolfram Milford, Michael Corke, Peter Brisbane Australia Robotics and Biology Laboratory Technische Universität Berlin Germany Department of Computer Science and Engineering University of Notre Dame IN United States DeepMind London United Kingdom Paul G. Allen School of Computer Science & Engineering University of Washington WA United States Oxbotica Ltd. Oxford United Kingdom UC Berkeley Department of Electrical Engineering and Computer Sciences CA United States Department of Computer Science University of Freiburg Germany

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and help fulfill the promising potentials of deep learning in robotics. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

Correction to: Editorial

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International Journal of Social robotics 2019年第6期15卷 1077-1077页

作者： Ge, Shuzhi Sam Wykowska, Agnieszka Khatib, Oussama Social Robotics Laboratory Smart Systems Institute Department of Electrical and Computer Engineering The National University of Singapore Singapore Singapore Institute for Future (IFF) Qingdao University Qingdao China Centre for Robotics UESTC Chengdu China Social Cognition in Human-Robot Interaction Istituto Italiano di Tecnologia Genoa Italy Artificial Intelligence Laboratory Department of Computer Science Stanford University Stanford USA

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