检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Chiang, Sheng-Hao Kuo, Jian-Jhih Shen, Shan-Hsiang Yang, De-Nian Chen, Wen-Tsuen Inst. of Information Science Academia Sinica Taipei Taiwan Dept. of Computer Science & Information Engineering National Taiwan University of Science & Technology Taipei Taiwan Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any inst.nt, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a |Dmax|1−Ε-competitive algorithm for any Ε > 0, where |Dmax| is the largest group size at any time. We design a |Dmax|competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN. Copyright © 2017, The Authors. All rights reserved.

关键词： Software defined networking

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on Big Data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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Opportunities to parallelize path planning algorithms for autonomous underwater vehicles

arXiv

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arXiv 2017年

作者： Eichhorn, Mike Kremer, Ulrich Inst. for Automation and Systems Engineering Ilmenau University of Technology Ilmenau98684 Germany Dept. of Computer Science Rutgers University PiscatawayNJ08854 United States

This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned. In this context, robust path planning algorithms will be described. These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems. The results presented here provide the basis for a future research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud computer (SCC). Modern multi and many core processors support a wide range of performance vs. energy tradeoffs that can be exploited in energyconstrained environments such as battery operated autonomous underwater vehicles. For this evaluation, the boards will be deployed within the Slocum glider, a commercially available, buoyancy driven autonomous underwater vehicle (AUV). Copyright © 2017, The Authors. All rights reserved.

关键词： Graphic methods

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Toward opportunistic services for the industrial Internet of Things

Toward opportunistic services for the industrial Internet of...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Giancarlo Fortino Claudio Savaglio Mengchu Zhou Dept. of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende CS Italy Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Services are expected to represent the real drivers for all Internet of Things (IoT) application scenarios, including Industrial IoT (IIoT). Indeed, rather than focusing only on products, enterprises are now investing on product-service hybrids to improve operational efficiency, boost productivity and, most importantly, build new markets, hence diversifying their revenues. In this direction, IoT service modeling is a crucial activity requiring a multi-facet in-dept. examination. In this paper, we first discuss the importance of IoT services by eliciting related benefits and challenges. Then, we tackle service modeling from both a developer-oriented (comprising highlevel descriptive metamodels and ontologies) and an enterprise-oriented (comprising operational models subject to formal verification and execution, such as extensions of the conventional Business Process Models, Petri Nets and WorkFlows) perspective, providing a conceptual mapping between these two complementary approaches. The proposed new paradigm of “Opportunistic IoT Service” extends the available models by explicitly considering essential features for service provision, i.e., dynamicity, context-awareness, co-location and transience.

关键词： Business Computational modeling Context modeling Analytical models Biological system modeling Ontologies Sensors

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Rate-distortion trade-offs in acquisition of signal parameters

Rate-distortion trade-offs in acquisition of signal paramete...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Miguel R. D. Rodrigues Nikos Deligiannis Lifeng Lai Yonina C. Eldar Dept. Electronic and Electrical Engineering University College London United Kingdom Dept. Electronics and Informatics Vrije Universiteit Brussel Belgium Dept. Electrical and Computer Engineering University of California Davis USA Dept. Electrical Engineering Technion - Israel Institute of Technology Israel

ISBN: (纸本)9781509041183

We consider problems where one wishes to represent a parameter associated with a signal source - subject to a certain rate and distortion - based on the observation of a number of realizations of the source signal. By reducing these indirect vector quantization problems to a standard vector quantization one, we provide a bound to the fundamental interplay between the rate and distortion in the large-rate setting. We specialize this characterization to two particular quantization scenarios: i) the representation of the mean of a multivariate Gaussian source;and ii) the representation of the eigen-spectrum of a multivariate Gaussian source. Numerical results compare our quantization approach to an approach where one recovers the parameters from the representation of the source signals itself: in addition to revealing that the characterization is sharp in the large-rate setting, the results also show that our approach offers considerable gains.

关键词： Signal Acquisition Signal Parameters Acquisition Rate-Distortion Vector Quantization Rate distortion Vector quantization signal acquisition signal parameter signal sources distortion

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Experimental demonstration of high-rate measurement-device-independent quantum key distribution over asymmetric channels

arXiv

引用

arXiv 2018年

作者： Liu, Hui Wang, Wenyuan Wei, Kejin Fang, Xiao-Tian Li, Li Liu, Nai-Le Liang, Hao Zhang, Si-Jie Zhang, Weijun Li, Hao You, Lixing Wang, Zhen Lo, Hoi-Kwong Chen, Teng-Yun Xu, Feihu Pan, Jian-Wei Shanghai Branch Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Shanghai201315 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Dept. of Electrical & Computer Engineering Dept. of Physics University of Toronto TorontoONM5S 3G4 Canada State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate all detector side channels and it is practical with current technology. Previous implementations of MDI-QKD all use two symmetric channels with similar losses. However, the secret key rate is severely limited when different channels have different losses. Here we report the results of the first high-rate MDI-QKD experiment over asymmetric channels. By using the recent 7-intensity optimization approach, we demonstrate >10x higher key rate than previous best-known protocols for MDI-QKD in the situation of large channel asymmetry, and extend the secure transmission distance by more than 20-50 km in standard telecom fiber. The results have moved MDI-QKD towards widespread applications in practical network settings, where the channel losses are asymmetric and user nodes could be dynamically added or deleted. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

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Interdependence between Power Grids and Communication Networks: A Resilience Perspective

Interdependence between Power Grids and Communication Networ...

引用

DRCN 2017 - Design of Reliable Communication Networks;13th International Conference

作者： Lucia Martins Rita Girao-Silva Luisa Jorge Alvaro Gomes Francesco Musumeci Jacek Rak Department of Electrical and Computer Engineering University of Coimbra Polytechnic Institute of Braganca Politecnico di Milano Dept. of Electronics Information and Bioengineering Department of Computer Communications Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology

ISBN: (纸本)9781509008940

Power network resilience is increasingly dependent on communication networks. Besides traditional generation, power networks need to accommodate increasingly high penetration levels of dispersed micro generation, mostly based on renewable sources, and increasing and challenging demand, such as electric vehicles. At the same time the deployment of enabling technologies throughout the power grid makes available new demand resources and storage capacity. These resources have to be jointly managed for efficient and resilient operation. Therefore a resilient communication network is mandatory for the smart control of these different resources. Communication networks in turn also rely on the power networks in spite of some power backup that they may have. This interdependence is especially critical in bad weather conditions where both networks can be severely affected, even more when they strongly depend on each other. This work describes ongoing research related with the interdependence between both networks regarding resilience, cascading failure effects, applicable models and simulation techniques.

关键词： Multicast communication Cognitive radio Routing protocols Delays Switches Channel allocation

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Very-high-energy gamma-ray emission from GRB 201216C detected by MAGIC 37

Very-high-energy gamma-ray emission from GRB 201216C detecte...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Suda, Yusuke Nava, Lara Noda, Koji Bošnjak, Željka Asano, Katsuaki Longo, Francesco Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, J.G. Green, D. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. López-Coto, R. López-Moya, M. López-Oramas, A. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Street number Merate Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia University of Trieste Department of Physics via Valerio 2 Trieste Italy INFN Sezione di Trieste via Valerio 2 Trieste Italy Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Università di Udine INFN Trieste UdineI-33100 Italy RomeI-00136 Italy ETH Zürich ZürichCH-8093 Switzerland Bellaterra BarcelonaE-08193 Spain The University of Tokyo Chiba Kashiwa277-8582 Japan Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil Università di Padova INFN PadovaI-35131 Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Università di Siena INFN Pisa SienaI-53100 Italy ZeuthenD-15738 Germany Max-Planck-Institut für Physik MünchenD-80805 Germany Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University o

Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe. Many aspects of GRB physics are still under debate, such as the origin of their gamma-ray emission above the GeV energy range. In 2019, MAGIC detected TeV gamma rays from the long GRB 190114C, whose emission can be well explained by synchrotron-self Compton emission by relativistic electrons. However, it is still unclear whether such a process is common in GRBs, given the reduced number of GRBs detected until now at the very high energies (VHE). GRB 201216C is a long GRB and is the second one detected by MAGIC in this energy range. After receiving the alert provided by Swift-BAT, MAGIC automatically slewed to the GRB position, starting observations 56 seconds after the GRB onset. In the offline analyses of the collected data, we confirmed the detection of gamma-ray emission with a significance above 5 sigma. Following measurements from optical facilities, the redshift of this GRB was estimated to be z = 1.1. This makes GRB 201216C the most distant object ever detected by ground-based gamma-ray telescopes. In this contribution we will show the analysis results of the MAGIC data, also in comparison with past detected GRBs in the same energy range. Finally, accounting for available multi-wavelength observations, we will comment on the possible origin of the VHE emission detected by MAGIC. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Germanium compounds

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Eco-aware Vehicle Routing in Urban Environments

Eco-aware Vehicle Routing in Urban Environments

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IEEE Symposium on computers and Communications

作者： Julian Dibbelt Damianos Gavalas Kalliopi Giannakopoulou Dionisis Kehagias Charalampos Konstantopoulos Spyros Kontogiannis Grammati Pantziou Dorothea Wagner Christos Zaroliagis Karlsruhe Institute of Technology Karlsruhe Germany Centre for Research and Technology Hellas/Information Technologies Institute (CERTH/ITI) Theesaloniki Greece Dept of Informatics Technological Educational Institution of Athens Patras Greece Dept of Product & Systems Design Engineering University of the Aegean Patras Greece Dept of Informatics University of Piraeus Patras Greece Dept of Computer Engineering & Informatics University of Patras Patras Greece Dept. of Computer Science & Engineering CTI Patras Greece

Mobility of people and goods in urban environments raises several quality and sustainability concerns. While ICTs have established the ground for developing intelligent transport services, their effective use for supporting cleaner urban mobility still represents a major research challenge. The eCOMPASS research project addressed this challenge through introducing new mobility concepts and establishing a methodological framework for route planning optimization, delivering a comprehensive set of innovative tools and services for end-users to enable eco-awareness in urban transport. eCOMPASS innovative tools are based on new algorithmic technology concerning tools and methods for vehicle routing (cars and vehicle fleets) and multimodal human mobility for city dwellers and tourists. eCOMPASS involved a generic architecture that considered all types and scenarios of human and goods mobility in urban environments minimizing their environmental impact. In this work, we report on the main scientific innovations and end-products of eCOMPASS for vehicle routing, including car route planning and vehicle fleets.

关键词： Urban mobility Environmental sustainability Route planning Car navigation Vehicle routing Traffic prediction

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