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

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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Basic algorithm for the coherent digital processing of the radio signals

Basic algorithm for the coherent digital processing of the r...

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International Conference on Space science and Communication (IconSpace)

作者： Alexey N. Glushkov Vladimir P. Litvinenko Boris V. Matveev Oleg V. Chernoyarov Alexandra V. Salnikova Dept. of Radio engineering Voronezh State Technical University Voronezh Russia Dept. of Radio engineering Devices and Antenna Systems National Research University “MPEI” Moscow Russia Dept. of Information Technologies and Computer-aided Design Voronezh State University of Architecture and Civil Engineering Voronezh Russia

We are to introduce the basic fast digital coherent demodulation algorithms for the radio signals with various modulation formats, the ones that require the minimum number of simple arithmetic operations. We are demonstrating how they provide the implementation of the demodulators of the phase-shift keyed and the amplitude-modulated signals. We have determined the expressions for the probabilities of error, and we show that in the case of white noise the considered devices provide noise stability close to the potential. The obtained results have been corroborated by statistical simulation. We are also to illustrate that the suggested algorithms can be practically implemented when supported by the signal processors and modern programmable logic devices.

关键词： Demodulation Signal processing algorithms Noise Phase shift keying Quadrature amplitude modulation Differential phase shift keying Stability analysis

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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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Amplitude estimate of the ultra wide band quasi radio signal with unknown initial phase and duration

Amplitude estimate of the ultra wide band quasi radio signal...

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International Conference on Space science and Communication (IconSpace)

作者： Yury E. Korchagin Oleg V. Chernoyarov Alexander A. Makarov Alexandra V. Salnikova Dept. of Radio physics Voronezh State University (VSU) Voronezh Russia Dept. of Radio engineering Devices and Antenna Systems National Research University “MPEI” Moscow Russia Dept. of Information Technologies and Computer-aided Design Voronezh State University of Architecture and Civil Engineering Voronezh Russia

We considered the new quasi-likelihood amplitude estimate algorithm for the ultra wide band quasi radio signal with unknown initial phase and inaccurately known duration against white Gaussian noise. Its block diagram was presented in single-channel units. We found the statistical characteristics of the introduced estimate and we also investigated the influence of the prior signal duration ignorance on the estimation efficiency. We determined the loss in accuracy of the amplitude estimate because of the duration-offset replica.

关键词： Signal to noise ratio Estimation Joints Random variables Accuracy Dispersion Physics

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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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A SPECTRAL UNMIXING METHOD BASED ON WAVELET WEIGHTED SIMILARITY

A SPECTRAL UNMIXING METHOD BASED ON WAVELET WEIGHTED SIMILAR...

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IEEE International Conference on Image Processing

作者： Qingyu Pang Jing Yu Weidong Sun State Key Lab. of Intelligent Technology & Systems Tsinghua National Lab. for Information Science & Technology Dept. of Electronic Engineering Tsinghua University College of Computer Science and Technology Beijing University of Technology

ISBN: (纸本)9781479983407

With the rapid development of the hyperspectral technology, spectra unmixing has received more and more attentions. In this paper, in order to measure the similarity between the extracted endmember spectra and the actual corresponding spectra of land covers and maintain the spectral absorption feature, a wavelet weighted similarity is presented. And by introducing it into the minimum distance constrained nonnegative matrix factorization method, a spectral unmixing method based on the wavelet weighted similarity is proposed. Base on the experiments with real hyperspectral image, the feasibility and real performance of our method has been examined and compared with that of unsupervised spectral unmixing method.

关键词： Spectral unmixing Wavelet weighted similarity Nonnegative matrix factorization

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GROUP-BASED HYPERSPECTRAL IMAGE DENOISING USING LOW RANK REPRESENTATION

GROUP-BASED HYPERSPECTRAL IMAGE DENOISING USING LOW RANK REP...

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IEEE International Conference on Image Processing

作者： Mengdi Wang Jing Yu Weidong Sun State Key Lab. of Intelligent Technology & Systems Tsinghua National Lab. for Information Science & Technology Dept. of Electronic Engineering Tsinghua University College of Computer Science and Technology Beijing University of Technology

ISBN: (纸本)9781479983407

For the hyperspectral image (HSI) denoising problem, we propose a group-based low rank representation (GLRR) method. A corrupted HSI is divided into overlapping patches and the similar patches are combined into a group. The group is denoised as a whole using low rank representation (LRR). Our method can employ both the local similarity within the patch and the nonlocal similarity across the patches within a group simultaneously, while nonlocal similar patches within the group can bring extra structure information for the corrupted patch, which makes the noise more significant to be detected as outliers. Since the uncorrupted patches have an intrinsic low-rank structure, LRR is employed for the denoising of the patch group. Both simulated and real data are used in the experiments. The effectiveness of our method is proved both qualitatively and quantitatively.

关键词： Denoising Hyperspectral image Low rank representation Nonlocal similarity Noise reduction hyperspectral imagery Patches images AKR1B10 gene outliers

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CubeSat Quantum Communications Mission

arXiv

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arXiv 2017年

作者： Oi, Daniel K.L. Ling, Alex Vallone, Giuseppe Villoresi, Paolo Greenland, Steve Kerr, Emma Macdonald, Malcolm Weinfurter, Harald Kuiper, Hans Charbon, Edoardo Ursin, Rupert SUPA Department of Physics University of Strathclyde John Anderson Building 107 Rottenrow East GlasgowG4 0NG United Kingdom Strathclyde Space Institute James Weir Building University of Strathclyde 75 Montrose Street GlasgowG1 1XJ United Kingdom Centre for Quantum Technologies National University of Singapore Block S15 Science Drive 2 Singapore117543 Singapore Dept. of Physics National University of Singapore 2 Science Drive 3 117542 Singapore Dipartimento di Ingegneria dell’Informazione Università degli Studi di Padova Via Giovanni Gradenigo 6 Padova35131 Italy Advanced Space Concepts Laboratory Mechanical and Aerospace Engineering University of Strathclyde James Weir Building 75 Montrose Street GlasgowG1 1XJ United Kingdom Scottish Centre of Excellence in Satellite Applications Technology and Innovation Centre 99 George Street GlasgowG1 1RD United Kingdom Department für Physik Ludwig-Maximilians-Universität Schellingstr. 4/III MunichD-80799 Germany Space Systems Engineering Aerospace Engineering Delft University of Technology Kluyverweg 1 Delft2629 Netherlands AQUA EPFL Rue de la Maladière 71b Case postale 526 Neuchâtel 2 LausanneCH-2002 Switzerland Delft University of Technology Mekelweg 4 Delft2628 Netherlands Institute for Quantum Optics and Quantum Information - Vienna Austrian Academy of Sciences Boltzmanngasse 3 Vienna1090 Austria

Quantum communication is a prime space technology application and offers near-term possibilities for long-distance quantum key distribution (QKD) and experimental tests of quantum entanglement. However, there exists considerable developmental risks and subsequent costs and time required to raise the technological readiness level of terrestrial quantum technologies and to adapt them for space operations. The small-space revolution is a promising route by which synergistic advances in miniaturization of both satellite systems.and quantum technologies can be combined to leap-frog conventional space systems.development. Here, we outline a recent proposal to perform orbit-to-ground transmission of entanglement and QKD using a CubeSat platform deployed from the International Space Station (ISS). This ambitious mission exploits advances in nanosatellite attitude determination and control systems.(ADCS), miniaturised target acquisition and tracking sensors, compact and robust sources of single and entangled photons, and high-speed classical communications systems. all to be incorporated within a 10kg 6litre mass-volume envelope. The CubeSat Quantum Communications Mission (CQuCoM) would be a pathfinder for advanced nanosatellite payloads and operations, and would establish the basis for a constellation of low-Earth orbit trusted-nodes for QKD service provision. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum cryptography

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Plexe: A platooning extension for Veins

Plexe: A platooning extension for Veins

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2014 IEEE Vehicular Networking Conference, VNC 2014

作者： Segata, Michele Joerer, Stefan Bloessl, Bastian Sommer, Christoph Dressler, Falko Cigno, Renate Lo Computer and Communication Systems Institute of Computer Science University of Innsbruck Austria Dept. of Information Engineering and Computer Science University of Trento Italy Distributed Embedded Systems Dept. of Computer Science University of Paderborn Germany

ISBN: (纸本)9781479976607

Cooperative driving in general and Cooperative Adaptive Cruise Control (CACC) or platooning in particular require blending control theory, communications and networking, as well as mechanics and physics. Given the lack of an integrated modeling framework and theory as well as the prohibitively high costs of using prototypes for what-if studies, simulation remains the fundamental instrument to evaluate entire cooperative driving systems. This work presents Plexe, an Open Source extension to Veins that offers researchers a simulation environment able to run experiments in realistic scenarios, taking into account physics and mechanics of the vehicles, communications and networking impairments, and Inter-Vehicle Communication (IVC) protocol stacks. Plexe is easily extensible and already implements protocols to support platooning and cooperative driving applications and several state of the art cruise control models. We describe the structure of the simulator and the control algorithms that Plexe implements and provide two use cases which show the potential of our framework as a powerful research tool for cooperative driving systems. © 2014 IEEE.

关键词： Blending

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The meter tells you are at home! Non-intrusive occupancy detection via load curve data

The meter tells you are at home! Non-intrusive occupancy det...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Guoming Tang Kui Wu Jingsheng Lei Weidong Xiao Dept. of Computer Science University of Victoria B.C Canada University of Victoria Victoria BC CA School of Computer and Information Engineering Shanghai University of Electric Power Shanghai China Sci. & Technol. on Information Systems Engineering Lab National University of Defense Technology Changsha China

Occupancy detection can greatly facilitate HVAC and lightning control for building energy saving. Sensor based occupancy detection is usually costly and may suffer from high false positive rates. As such, occupancy detection using load curve data has been proposed. Such type of methods, however, normally relies on tedious and nontrivial model training process. To overcome this pitfall, we develop a simple, non-intrusive occupancy detection approach that does not require any model training and only uses load curve data and readily-available appliance knowledge. The method consists of three main steps: i) the appliances' mode states are firstly decoded via a carefully designed total variation minimization problem; ii) the human actions are recovered with a-priori knowledge of human-activated switching events; iii) the occupancy states are then inferred based on the recovered human actions along with empirical association rules. We evaluate our approach and compare with existing methods with real-world data. The results show that our approach can achieve similar performance to those using supervised machine learning.

关键词： Home appliances Switches Power demand Buildings Training Smart grids Hidden Markov models

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