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International Conference on Control Communication & Computing India (ICCC)

作者： V. Sreejith K.R. Anupama Lucy J Gudino R. Suriyadeepan Dept. of Computer Science and Information Systems BITS-Pilani K K Birla Goa India Dept. of Electrical Electronics and Instrumentation Engineering BITS-Pilani K K Birla Goa India

Streaming multimedia data in WSN is a challenge due to constraints such as bandwidth, energy and delay. Moreover, it is not economical to have multimedia sensors integrated with static nodes in the field. In this paper, we propose the use of mobile sensor nodes, which are equipped with multimedia sensors for event monitoring. These mobile entities are capable of streaming high bandwidth data by splitting it and routing the packet to the base station using Frequency Division Multiplexer (FDM) modules. This is done with the support of multi-channel disjoint path routing aided with the static WSN deployed. Experiments were done using MicaZ and TelosB motes to evaluate the performance with the help of our custom designed robotic vehicle. The paper adopts a node disjoint multi-path routing protocol for establishing parallel paths between the sender and the receiver. Experimental results indicate that the proposed approach can be effectively used for transferring high bandwidth data to the base station.

关键词： Mobile nodes Base stations Robot sensing systems Multimedia communication Streaming media Wireless sensor networks

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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Adaptive Robotic Interceptions of Moving Targets with a Target Motion Estimation-Based Visual Tracking Controller

Adaptive Robotic Interceptions of Moving Targets with a Targ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yushing Cheung Jenn-Jier James Lien Ya-Ting Huang Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Software Development Department Tongtai Machine & Tool Co. Ltd Kaohsiung Taiwan

ISBN: (纸本)9781479986989

An automated assembly task on production lines requires robotic manipulation of a target when the target is moving. Due to complicated environments and multi-tasking, vision occlusion and uncertainty of dynamics of a gripper/target may happen. Therefore, we developed a vision guided robotic system composed of a target motion estimation-based visual tracking controller and an adaptive controller. The target motion estimation-based visual tracking controller enables the system to estimate target positions and velocities and compute desired robotic positions and velocities based on previous estimates even if the target is not viewed occasionally. Moreover, a robotic end-effector is controlled with the adaptive controller to pursue and catch the target even if the target and gripper parameters are not exactly known. In experiments, interceptions of the target moving at different speeds by using different grippers based on visual feedback were presented to demonstrate good performance on efficiency and robustness of the proposed method.

关键词： Robots Target tracking Yttrium Conferences Cybernetics Visualization

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Evaluating Bug Finders -- Test and Measurement of Static Code Analyzers

Evaluating Bug Finders -- Test and Measurement of Static Cod...

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IEEE/ACM International Workshop on Complex Faults and Failures in Large software systems (COUFLESS)

作者： Aurelien Delaitre Bertrand Stivalet Elizabeth Fong Vadim Okun Dept. of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Software and Systems Division IT Laboratory National Institute of Standards and Technology Gaithersburg MD USA

software static analysis is one of many options for finding bugs in software. Like compilers, static analyzers take a program as input. This paper covers tools that examine source code - without executing it - and output bug reports. Static analysis is a complex and generally undecidable problem. Most tools resort to approximation to overcome these obstacles and it sometimes leads to incorrect results. Therefore, tool effectiveness needs to be evaluated. Several characteristics of the tools should be examined. First, what types of bugs can they find? Second, what proportion of bugs do they report? Third, what percentage of findings is correct? These questions can be answered by one or more metrics. But to calculate these, we need test cases having certain characteristics: statistical significance, ground truth, and relevance. Test cases with all three attributes are out of reach, but we can use combinations of only two to calculate the metrics. The results in this paper were collected during Static Analysis Tool Exposition (SATE) V, where participants ran 14 static analyzers on the test sets we provided and submitted their reports to us for analysis. Tools had considerably different support for most bug classes. Some tools discovered significantly more bugs than others or generated mostly accurate warnings, while others reported wrong findings more frequently. Using the metrics, an evaluator can compare candidates and select the tool that aligns best with his or her objectives. In addition, our results confirm that the bugs most commonly found by tools are among the most common and important bugs in software. We also observed that code complexity is a major hindrance for static analyzers and detailed which code constructs tools handle well and which impede their analysis.

关键词： software Measurement Production Complexity theory computer bugs Java NIST

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PB-09Alpha-synuclein Binds Unconventional Microtubules That Have a Unique Function

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Microscopy 2017年第SUPPL_1期66卷 i37-i37页

作者： Toba, Shiori Jin, Mingyue Yamada, Masami Matsumoto, Sakiko Yasunaga, Takuo Fukunaga, Yuko Miyazawa, Atsuo Kojima, Hiroaki Arai, Yoshiyuki Nagai, Takeharu Hirotsune, Shinji Osaka City Univ. Grad. Sch. of Medicine Present address: Dept. of Emergency Medical Technology Hirosaki University of Health and Welfare Junior College Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan JST-SENTAN JST-CREST Grad. Sch. of Life Science Univ. of Hyogo RSC-Univ. of Hyogo Leading Program Center RIKEN SPring-8 Center Advanced ICT Research Inst. National Inst. of Information and Communications Technology and Inst. of Scientific and Industrial Research Osaka Univ. Japan.

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A new scheme for proactive out of band signaling solution for IP traceback in Wireless Mesh Network

A new scheme for proactive out of band signaling solution fo...

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International Conference on computer, information and Telecommunication systems (CITS)

作者： Mouna Gassara Faouzi Zarai Ikbel Daly Mohammad S. Obaidat Kuei-Fang Hsiao National engineering School of Sfax (ENIS) University of Sfax Tunisia Computer Science and Software Engineering Department Monmouth University NJ USA Dept. of Information Management Ming Chuan University Taoyuan Taiwan

Since Wireless Mesh Networks (WMNs) have received great attention and momentum, security has become a crucial issue for them. Denial of Service attack (DoS) represents a serious threat on WMNs due to their plug-and-play architecture. Unfortunately, a DoS attack still very hard to stop due to the uncontrolled spoofing of source IP addresses. The best solution to this is to fix the problem at its root by guessing the source of attack. As a solution to this dilemma, numerous approaches have been suggested to support IP traceability. In this work, we focus on Wireless Mesh Network WMN (IEEE 802.11s standard). We design a signaling architecture, based on previous works, in order to trace anonymous IP packets back towards their source(s), where a simple security-oriented signaling protocol permits specialized signaling entities to exchange reliable signaling information that allows performing a simple and efficient traceback. This solution represents a novel and efficient approach to deal with the traceback problem in WMN environments.

关键词： IP networks IEEE 802.11 Standard computer architecture Wireless mesh networks Protocols computer crime

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Learning automaton based context oriented middleware architecture for precision agriculture

Learning automaton based context oriented middleware archite...

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International Conference on computer, information and Telecommunication systems (CITS)

作者： P. Venkata Krishna S. Sivanesan Sudip Misra Mohammad S. Obaidat Department of Computer Science Sri Padmavathi Mahila Visvavidyalayam Tirupati India School of Information Technology Indian Institute of Technology Kharagpur India School of Computing Science and Engineering VIT University Vellore India Dept. of Computer Science& Software Engineering Monmouth University NJ USA

The continuous alteration of living landscape in earth due to the greatest impact of information technology has converted the whole earth into a small digital village. That means anyone could share information in any form with any other person residing anywhere in the earth. With the availability of information and communication technology like cloud computing, heterogeneous networking, crowd sensing, web services and data mining, anywhere and anytime information sharing is possible, but this will bring out lot of challenges like incompatibility in standards, data portability, data aggregation, data dissemination, differential context and communication overhead. The ICT has changed many aspects of human lifestyle, work places and living spaces. However, there is one sector namely agriculture which has been deprived of the real advantages of ICT. Hence, there exists a digital divide between farming industry and other industries. Farming industry consumes large amount of natural resources such as water, energy and fertilizers that could escalate issues such as global warming, soil degradation and depletion of ground water to the next level. To reduce the global warming effect, farming industry needs to be integrated with relevant technologies. This paper proposes ubiquitous context oriented middleware architecture for precision agriculture (LA-COMPa) to solve major issues such as waste of water, improper application of fertilizer, choice of wrong crops and season, poor yield and lack of marketing.

关键词： Context Learning automata Middleware Fertilizers Manuals Soil

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Privacy Preserving String Matching for Cloud Computing

Privacy Preserving String Matching for Cloud Computing

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International Conference on Distributed Computing systems

作者： Bruhadeshwar Bezawada Alex X. Liu Bargav Jayaraman Ann L. Wang Rui Li National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Dept. of Computer Science and Engineering Michigan State University East Lansing MI USA International Institute of Information Technology Hyderabad India College of Information Science and Engineering Hunan University China

Cloud computing has become indispensable in providing highly reliable data services to users. But, there are major concerns about the privacy of the data stored on cloud servers. While encryption of data provides sufficient protection, it is challenging to support rich querying functionality, such as string matching, over the encrypted data. In this work, we present the first ever symmetric key based approach to support privacy preserving string matching in cloud computing. We describe an efficient and accurate indexing structure, the PASS tree, which can execute a string pattern query in logarithmic time complexity over a set of data items. The PASS tree provides strong privacy guarantees against attacks from a semi-honest adversary. We have comprehensively evaluated our scheme over large real-life data, such as Wikipedia and Enron documents, containing up to 100000 keywords, and show that our algorithms achieve pattern search in less than a few milliseconds with 100% accuracy. Furthermore, we also describe a relevance ranking algorithm to return the most relevant documents to the user based on the pattern query. Our ranking algorithm achieves 90%+ above precision in ranking the returned documents.

关键词： Pattern matching Indexes Cryptography Cloud computing Servers Privacy Complexity theory

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ST-Diary: A multimedia authoring environment for crowdsourced spatio-temporal events 8

ST-Diary: A multimedia authoring environment for crowdsource...

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8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015

作者： Ahmad, Akhlaq Afyouni, Imad Murad, Abdullah Rahman, Md. Abdur Rehman, Faizan Ur Sadiq, Bilal Basalamah, Saleh Wahiddin, Mohamed Ridza Kulliyah of Information and Communication Technology International Islamic University Makkah Saudi Arabia College of Engineering and Islamic Architecture Umm Al Qura University Makkah Saudi Arabia KACST GIS Technology Innovation Center Umm Al Qura University Makkah Saudi Arabia Innovation and Entrepreneurship Institute Umm Al Qura University Malaysia College of Computer and Information Systems Umm Al Qura University Makkah Saudi Arabia Dept. of Computer Science LIG University of Grenoble Alpes France

ISBN: (纸本)9781450339759

The intensive use of social media through mobile devices has leveraged the development of digital diary applications that keep track of social events as well as geotagged multimedia content. In a large crowd where users with cultural diversity perform spatiotemporal activities, such geotagged multimedia content facilitates users' navigation through points of interest (POI) based on their preferences. This work presents a crowdsourced geo-spatial multimedia data aggregation tool that allows users to develop diary chapters relevant to forthcoming users' spatio-temporal activities. Our proposed solution provides users with the ability to add POIs through an authoring environment with multiple dimensions, such as spatio-temporal filters, multimedia categories, and event types. Specific application domains such as emergency situations, leisure trips, journalism, and tourism can take benefit of this technique. This authoring environment also visualizes geo-spatial multimedia content for collocated points of interest (CPOI) with moving users' timelines. We plan to integrate our proposed authoring environment as a proof of concept into our existing large-scale crowdsourcing environment that is envisioned to support millions of users during the Hajj 2015 event.

关键词： Crowdsourcing

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