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2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017

作者： Zamansky, Anna Roshier, Amanda Mancini, Clara Collins, Emily C. Hall, Carol Grillaert, Katie Morrison, Ann North, Steve Wirman, Hanna Information Systems Department University of Haifa Israel School of Veterinary Medicine and Science University of Nottingham United Kingdom Animal-Computer Interaction Lab Open University Milton Keynes United Kingdom Sheffield Robotics University of Sheffield United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University United Kingdom Messerli Research Institute University of Veterinary Medicine Medical University of Vienna University of Vienna Austria Department of Architecture Design and Media Technology Aalborg University Denmark Mixed Reality Laboratory University of Nottingham United Kingdom School of Design Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450346566

Animal-computer Interaction (ACI) is a new and quickly developing discipline, which is closely related to HCI and is making reference to some of its theoretical frameworks and research methodologies. The first edition of the Workshop on Research Methods in ACI (RM4ACI) was co-located with the Third International Conference on Animal-computer Interaction, which took place in Milton-Keynes, UK in November 2016. This paper presents an overview of the workshop, including insights from discussions on some of the challenges faced by the ACI community as it works to develop ACI as a discipline, and on important opportunities for cross-fertilization between HCI and ACI that the HCI community could consider. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Animals

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AUV dead-reckoning navigation based on neural network using a single accelerometer 11

AUV dead-reckoning navigation based on neural network using ...

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Xie, Yan-Xin Liu, Jun Hu, Cheng-Quan Cui, Jun-Hong Xu, Hongli State Key Laboratory of Robotics CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781450346375

The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.

关键词： Accelerometers

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Whole-body motion planning for humanoid robots with heuristic search

Whole-body motion planning for humanoid robots with heuristi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Ali Athar Abdul Moeed Zafar Rizwan Asif Armaghan Ahmad Khan Fahad Islam Yasar Osman Hasan Robotics & Intelligent Systems Engineering (RISE) Lab School of Mechanical & Manufacturing Engineering (SMME) National University of Sciences & Technology (NUST) Islamabad Pakistan School of Electrical Engineering & Computer Sciences (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781509037636

The task of whole-body motion planning for humanoid robots is challenging due to its high-DOF nature, stability constraints, and the need for obstacle avoidance and movements that are efficient. Over the years, various approaches have been adopted to solve this problem such as bounding-box models and jacobian-based techniques. More commonly though, sampling-based algorithms are employed for this task since they perform admirably well in high-dimensional spaces. As an alternative, search-based planners offer improvements in terms of optimality and consistency of the solution. However, they are normally considered impractical for high-dimensional motion planning. In this paper, we present a heuristic search-based motion planning framework for humanoid robots that circumvents the drawbacks traditionally associated with search-based planners while catering to the specific requirements of humanoid motion planning. This is achieved primarily through a combination of informative yet computationally inexpensive heuristics, carefully crafted motion primitives as atomic actions, and a whole body inverse kinematics solver for achieving desired end effector orientations. The experimental results show the ability of our framework to perform complex motion planning tasks quickly and efficiently.

关键词： Planning Humanoid robots Collision avoidance Heuristic algorithms Legged locomotion Foot

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Application of wireless technology for a vision based rehabilitation system

Application of wireless technology for a vision based rehabi...

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IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Arpita Ray Sarkar G. Sanyal Somajyoti Majumder Dept. of Computer Sc. & Engg. National Institute of Technology Durgapur Durgapur India Surface & Field Robotics Lab CSIR — Central Mech. Engg. Research Institute Durgapur India

ISBN: (纸本)9781509010486

Though several assistive technologies are available for home-based rehabilitation of stroke patients, people are not using the same for several reasons. Lack of technical competence, chance of electrocution is some of such issues. The cables/ wires of different instruments, measuring devises, rehabilitation systems are often the source of infection; causes of electrocution, discomfort and immobility. This has led to implement wireless technology/ system for medical applications. Accordingly researches are going on to use wireless technologies for different purposes of rehabilitation. The present work uses a wireless night vision camera for gaze tracking of the patient for 24×7 support eliminating the use of cables/ wires and thus reducing the chances of infections and other hazardous. The camera along with a power supply (battery) serves as a standalone unit for ready deployment. The control computer can be set up at a distance even out of the cabin and helps the physiotherapists and healthcare staffs to monitor and assist several patients remotely at a time. The experiments have been carried out in both day and night with users wearing spectacles or no spectacles. Promising results have been obtained in spite of several issues of data loss, IR glaring etc. Several applications of wireless technologies have been attempted, but use of such approach has never been tried earlier. This system has huge potential for application for day and night support and rehabilitation of stroke patients.

关键词： Wireless communication Wireless sensor networks Cameras computers Monitoring Ad hoc networks Medical services

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Editorial

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International Journal of Social robotics 2015年第1期7卷 1-2页

作者： Ge, Shuzhi Sam Khatib, Oussama and the Robotics Research Lab Department of Electrical & Computer Engineering The National University of Singapore Singapore Singapore University of Electronic Science and Technology of China Chengdu China Artificial Intelligence Laboratory Department of Computer Science Stanford University StanfordCA United States

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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Self-Organized Construction with Continuous Building Material: Higher Flexibility Based on Braided Structures

Self-Organized Construction with Continuous Building Materia...

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IEEE International Workshops on Foundations and Applications of Self Systems

作者： Mary Katherine Heinrich Mostafa Wahby Mohammad Divband Soorati Daniel Nicolas Hofstadler Payam Zahadat Phil Ayres Kasper Støy Heiko Hamann Centre for IT and Architecture Royal Danish Academy Copenhagen Denmark Heinz Nixdorf Institute University of Paderborn Germany Universitat Paderborn Paderborn Nordrhein-Westfalen DE Artificial Life Lab of the Department of Zoology Karl-Franrens University Graz Austria Robotics Evolution and Art Lab IT University of Copenhagen Denmark Department of Computer Science Chemnitz University of Technology Germany

Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament interlacing.

关键词： Robot sensing systems Context Mobile robots Switches Carbon

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Exploiting the use of machine learning in two different sensor network architectures for indoor localization

Exploiting the use of machine learning in two different sens...

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IEEE International Conference on Industrial technology (ICIT)

作者： Eduardo Carvalho Bruno S. Faiçal Geraldo P. R. Filho Patrícia A. Vargas Jó Ueyama Gustavo Pessin Institute of Exact and Natural Sciences Federal University of Pará PA Brazil Institute of Mathematical and Computer Sciences University of São Paulo São Carlos SP Brazil Robotics Laboratory Heriot-Watt University Edinburgh UK Applied Computing Lab Vale Institute of Technology PA Brazil

Indoor localization has been an active research area for the last two decades. This emerged in the context of providing a mobile robot the capability to conduct navigation tasks in indoor environments. Although the sensing technologies and techniques proposed for indoor robot localization have proven to be reliable solutions, these cannot be adopted as a solution to people or object localization for indoor environments, particularly, due to their high computational cost and power requirements. In order to mitigate these issues, a low-power consumption sensing technology, based on the strength of WiFi signals, is being studied. Nevertheless, a concern when working with these signals is their vulnerability to interference. This paper exploits the use of machine learning is two different architectures for localization and present how a data filtering technique can alleviate interferences. A step into a fault tolerance approach is also given, presenting that the system can maintain certain reliability even losing some of its parts.

关键词： Indoor environments Estimation Machine learning algorithms Robot sensing systems Buildings Wireless sensor networks Support vector machines

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Earth image classification design using unmanned Aerial vehicle

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Telkomnika (Telecommunication Computing Electronics and Control) 2015年第3期13卷 1021-1028页

作者： Prasetio, Barlian Henryranu Supianto, Ahmad Afif Setiawan, Gembong Edhi Setiawan, Budi Darma Cholissodin, Imam Akbar, Sabriansyah R. Computer System and Robotics Lab Program of Information Technology and Computer Science University of Brawijaya Malang65145 Indonesia Intelligent Computing and Multimedia Lab Program of Information Technology and Computer Science University of Brawijaya Malang65145 Indonesia Computer Networking Lab Program of Information Technology and Computer Science University of Brawijaya Malang65145 Indonesia

The research aims to build software that can perform the classification of earth image from UAV (Unmanned Aerial Vehicle) monitoring. The Image converted into YUV format then classified using Fuzzy Support Vector Machine (FSVM). UAVs will be used for monitoring as follows: (1) the control station, which used to send or receive data, and display the data in graphical form, (2) payload, camera captured images and send to the control station, (3) communication system using TCP/IP protocol, and (4) UAV, using X650 quad copter products. The image of the monitoring carried out on the UAV sized 256 x 256 pixels with 450 training data. It is 16x16 pixel image data. Tests performed to classify the image into 3 classes, namely agricultural area, residential area, and water area. The highest accuracy value of 77.69% obtained by the number of training data as much as 375. © 2015 Universitas Ahmad Dahlan.

关键词： Image classification

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