Dipole Excitations Below the Neutron Separation Threshold in Neutron-Rich Tin Isotopes

Total Page:16

File Type:pdf, Size:1020Kb

Dipole Excitations Below the Neutron Separation Threshold in Neutron-Rich Tin Isotopes Dipole excitations below the neutron separation threshold in neutron-rich Tin isotopes. Zanetti, Lorenzo (2020) DOI (TUprints): https://doi.org/10.25534/tuprints-00013483 Lizenz: CC-BY-SA 4.0 International - Creative Commons, Attribution Share-alike Publikationstyp: Ph.D. Thesis Fachbereich: 05 Department of Physics Quelle des Originals: https://tuprints.ulb.tu-darmstadt.de/13483 Kernreaktionen GSI Helmholtzzentrum f¨ur Schwerionenforschung GmbH Physik Dipole excitations below the neutron separation threshold in neutron-rich Tin isotopes. Vom Fachbereich Physik der Technischen Universit¨atDarmstadt zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigte Dissertation von M. Sc. Lorenzo Zanetti aus Mailand Darmstadt 2019 D17 Dipole excitations below the neutron separation threshold in neutron-rich Tin isotopes. Referent: Prof. Dr. Thomas Aumann Korreferent: Prof. Dr. Joachim Enders Tag der Einreichung: 19.11.2019 Tag der m¨undlichen Pr¨ufung: 11.12.2019 Zanetti, Lorenzo: Dipole excitations below the neutron separation threshold in neutron-rich Tin isotopes. Darmstadt, Technische Universit¨atDarmstadt, Year thesis published in TUprints: 2020 Date of the viva voce: 11.12.2019 Published under CC BY-SA 4.0 Interantional https://creativecommons.org/licenes/ To the 2+ state of Tin-132 Abstract Among the many ways to describe and predict the behaviour of a nucleus, we find the nuclear equation of state (EoS). This expression aims to account for the properties of nuclear matter from common run-off-the-mill nuclei, such as those found in nature, to neutron stars and nuclei far from stability synthesized in laboratories. Around the density nuclear matter would assume if it were infinite and constituted in equal parts by neutrons and protons, the nuclear equation of state can be expanded in a Taylor series: the first terms of this series define the behaviour of the equation of state. Of particular interest is the second order term, known as the symmetry energy. Because it is not experimentally accessible, though, a direct measurement cannot be performed and other observables have to be individuated. One of these observables is the dipole polarizability, indicated with αD of the nucleus; this quantity correlates with the thickness of the neutron skin that forms around nuclei with a marked excess of neutrons and, thus, has the potential to put a constraint on the first order coefficient of symmetry energy's Taylor series expansion around the saturation density. This term is known as the \slope" and indicated with the letter L. The dipole polarizability can be obtained by integrating the strength of the nuclear response on all energies; this integral, though, is weighted by the energy: the response at lower energies plays, proportionally, a bigger role than that at higher ones. The aim of this work is to obtain a strength distribution for a neutron-rich isotope of Tin, 132Sn, and thus measure the dipole polarizability. To accomplish this, the data acquired at the GSI facility by the R3B collaboration, in Darm- stadt, have been used and a novel method for profiling the strength of the dipole response of a nucleus from a noisy spectrum has been demonstrated to be promising and, furthermore, it is expected to be portable to other isotopes and experiments where a gamma array detector is used. Zusammenfassung Unter den zahlreichen M¨oglichkeiten, das Verhalten eines Atomkerns zu beschreiben und vorherzusagen, befindet sich die nukleare Zustandsgleichung (EoS). Die EoS zielt darauf ab, die Eigenschaften von Kernmaterie zu erkl¨aren,welche von gew¨ohnlichen Kernen, wie sie in der Natur vorkom- men, bis zu Neutronensternen und Kernen, welche in Laboratorien synthetisiert werden und weit von der Stabilit¨atentfernt sind, reichen. Wenn bei einer bestimmten Dichte der Kernmaterie angenommen wird, dass sie unendlich w¨are und zu gleichen Teilen aus Neutronen und Protonen besteht, dann kann die nukleare Zustands- gleichung in einer Taylor-Reihe erweitert werden: Die ersten Terme dieser Reihe definieren das Verhalten der Zustandsgleichung. Von besonderem Interesse ist der Term zweiter Stufe, der als Symmetrieenergie bekannt ist. Da dieser Term jedoch nicht experimentell zug¨anglich ist, kann keine direkte Messung durchgef¨uhrtwerden und andere Observablen m¨ussenindividualisiert werden. Eine dieser Observablen ist die Dipolpolarisierbarkeit des Kerns, angegeben mit αD. Diese Gr¨oßekorreliert mit der Dicke der Neutronenhaut, die sich um die Kerne mit einem deut- ¨ lichen Uberschuss an Neutronen bildet. αD besitzt die M¨oglichkeit, eine Einschr¨ankungf¨urdie Taylorreihenausdehnung des Koeffizienten erster Ordnung der Symmetrieenergie um die S¨atti- gungsdichte zu setzen. Dieser Term wrid als \Steigung" und mit der Variablen L bezeichnet. Die Dipolpolarisierbarkeit kann erhalten werden, indem die St¨arke der Dipolantwort des Kernes auf alle Energien integriert wird. Dieses Integral wird jedoch mit der Energie gewichtet: Die Reaktion bei niedrigeren Energien spielt proportional eine gr¨oßereRolle als die bei h¨oheren. Ziel dieser Arbeit ist es, eine Dipolst¨averteilung f¨urein neutronenreiches Isotop von Zinn, 132Sn, zu erhalten und damit die dipole polarizability zu messen. Zu diesem Zweck wurden die an der GSI bei R3B in Darmstadt gewonnenen Daten verwendet, um zu zeigen, dass eine neuartige Methode zur Ermittlung der St¨arke der Dipolreaktion eines Kerns aus einem verrauschten Spektrum vielversprechend ist. Diese Methode kann auf andere Isotope und Experimente, bei denen ein Gamma-Array-Detektor verwendet wird, ¨ubertragen werden. Contents 1 Introduction 1 1.1 Motivation . .1 1.2 Theoretical overview . .2 1.2.1 The nuclear equation of state . .2 1.2.2 L and J parameters . .2 1.2.3 Relationship with αD, the static dipole polarizability . .3 1.2.4 Coulomb excitation . .5 1.2.5 Coulomb cross section and photo absorption cross section . .6 1.2.6 What about the oscillator strength? . .6 1.3 Experimental setup . .7 1.3.1 Beam . .8 1.3.2 Pre target tracking . 10 1.3.3 The Darmstadt-Heidelberg Crystal Ball . 13 1.3.4 Reaction fragment tracking . 22 1.3.5 Magnet assembly . 26 1.3.6 Targets and beams . 27 2 Analysis 28 2.1 Instruments . 28 2.1.1 Land02 . 28 2.1.2 The Toolkit . 29 2.2 Energy (and time) calibration . 42 2.2.1 Crystal Ball calibration . 42 2.3 Event selection . 43 2.3.1 Trigger matrices . 43 2.3.2 Incoming isotope identification . 45 2.3.3 Outgoing fragment . 48 2.4 Data preparation . 51 2.4.1 Front and back of the Crystal Ball . 51 2.4.2 Crystal thresholds . 53 2.4.3 Empty target contributions . 54 2.5 Simulations . 57 2.5.1 General setup and sbkg ........................... 57 2.5.2 The atomic background . 58 2.5.3 Event generation and abkg .......................... 58 CONTENTS 2.5.4 Experimental condition for the atomic background . 59 2.6 The whole background picture . 62 2.7 Calculations . 63 2.7.1 Cut efficiency, specificity and uncertainties . 63 2.7.2 Reaction probability . 65 2.7.3 Cross section . 67 2.7.4 Virtual photon number . 68 2.7.5 Coulomb excitation cross section from n-pole strength . 72 2.7.6 αD from dipole strength and photo absorption cross section distributions 74 3 Results 76 3.1 A landmark: 132Sn 2+ state . 76 3.1.1 Efficiencies involved . 77 3.1.2 Comparison with the data . 77 3.2 Target excitation . 80 3.2.1 Sensitivity (short) . 83 3.3 \Deconvolution" of 132Sn cascades . 83 3.3.1 Nuclear realisations . 85 3.4 Dipole polarizability . 87 3.4.1 Is it really just the dipole strength? . 88 4 Conclusions and Outlook 90 4.1 Sensitivity and target choice . 90 4.1.1 Sensitivity . 90 4.1.2 Target choice . 92 4.1.3 Outlook . 93 A Toolkit 95 A.1 Land02 . 95 A.2 XB programs . 95 A.2.1 Why not ROOT? . 95 A.2.2 Data structures . 96 A.2.3 File format . 97 A.2.4 Data conversion . 97 A.3 Octave tools . 98 A.3.1 "Manual fitter” . 99 B Clustering 101 B.1 What is \clustering"? . 101 B.2 Specificity of our problem . 101 CONTENTS B.2.1 Reasonable assumptions . 101 B.3 Nearest neighbour . 102 B.3.1 Problem of the nearest neighbour . 102 B.4 "Beading" . 104 B.4.1 Number of iterations . 105 B.4.2 Advantages of the beading algorithm . 105 B.4.3 Disadvantages of this algorithm . 106 C Thresholds of the Crystal Ball 107 D R3BRoot 109 D.1 FairSoft and FairRoot . 109 D.1.1 FairSoft . 109 D.1.2 FairRoot . 111 E sbkg 112 E.1 Simulation setup . 112 E.2 R3BRoot extensions . 112 E.2.1 Improved target geometry . 113 E.2.2 Tracking detectors . 113 F abkg 115 F.1 Atomic background spectrum breakup . 120 G Trigger flags and Tpat 126 List of Figures 138 List of Tables 139 Chapter 1 Introduction In this work, I'll attempt to extract the dipole polarizability of 132Sn from its Coulomb excita- tion. Because this quantity is proportional to an integral inversely weighted by the excitation energy, 1 I will concentrate on the region from zero to the (first) neutron separation threshold, Sn. 1.1 Motivation Among the many ways to describe and predict the behaviour of a nucleus, we find the nuclear equation of state (EoS). This expression aims to account for the properties of nuclear matter from common run-off-the-mill nuclei, such as those found in nature, to neutron stars and nuclei synthesized in laboratories far from stability. As we will see in the following, more detailed sections, the nuclear equation of state is.
Recommended publications
  • Open Source Used in Influx1.8 Influx 1.9
    Open Source Used In Influx1.8 Influx 1.9 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-1178791953 Open Source Used In Influx1.8 Influx 1.9 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-1178791953 Contents 1.1 golang-protobuf-extensions v1.0.1 1.1.1 Available under license 1.2 prometheus-client v0.2.0 1.2.1 Available under license 1.3 gopkg.in-asn1-ber v1.0.0-20170511165959-379148ca0225 1.3.1 Available under license 1.4 influxdata-raft-boltdb v0.0.0-20210323121340-465fcd3eb4d8 1.4.1 Available under license 1.5 fwd v1.1.1 1.5.1 Available under license 1.6 jaeger-client-go v2.23.0+incompatible 1.6.1 Available under license 1.7 golang-genproto v0.0.0-20210122163508-8081c04a3579 1.7.1 Available under license 1.8 influxdata-roaring v0.4.13-0.20180809181101-fc520f41fab6 1.8.1 Available under license 1.9 influxdata-flux v0.113.0 1.9.1 Available under license 1.10 apache-arrow-go-arrow v0.0.0-20200923215132-ac86123a3f01 1.10.1 Available under
    [Show full text]
  • Easybuild Documentation Release 20210907.0
    EasyBuild Documentation Release 20210907.0 Ghent University Tue, 07 Sep 2021 08:55:41 Contents 1 What is EasyBuild? 3 2 Concepts and terminology 5 2.1 EasyBuild framework..........................................5 2.2 Easyblocks................................................6 2.3 Toolchains................................................7 2.3.1 system toolchain.......................................7 2.3.2 dummy toolchain (DEPRECATED) ..............................7 2.3.3 Common toolchains.......................................7 2.4 Easyconfig files..............................................7 2.5 Extensions................................................8 3 Typical workflow example: building and installing WRF9 3.1 Searching for available easyconfigs files.................................9 3.2 Getting an overview of planned installations.............................. 10 3.3 Installing a software stack........................................ 11 4 Getting started 13 4.1 Installing EasyBuild........................................... 13 4.1.1 Requirements.......................................... 14 4.1.2 Using pip to Install EasyBuild................................. 14 4.1.3 Installing EasyBuild with EasyBuild.............................. 17 4.1.4 Dependencies.......................................... 19 4.1.5 Sources............................................. 21 4.1.6 In case of installation issues. .................................. 22 4.2 Configuring EasyBuild.......................................... 22 4.2.1 Supported configuration
    [Show full text]
  • Bringing Probabilistic Programming to Scientific Simulators at Scale
    Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, and Frank Wood SC19, Denver, CO, United States 19 November 2019 Simulation and HPC Computational models and simulation are key to scientific advance at all scales Particle physics Nuclear physics Material design Drug discovery Weather Climate science Cosmology 2 Introducing a new way to use existing simulators Probabilistic programming Simulation Supercomputing (machine learning) 3 Simulators Parameters Outputs (data) Simulator 4 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output 5 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output 6 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output WE NEED THE INVERSE! 7 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output Inference: ● Find parameters that can produce (explain) observed data ● Inverse problem ● Often a manual process 8 Simulators Parameters Outputs (data) Inferred Simulator Observed data parameters Gene network Gene expression 9 Simulators Parameters Outputs (data)
    [Show full text]
  • Advances in Electron Microscopy with Deep Learning
    Advances in Electron Microscopy with Deep Learning by Jeffrey Mark Ede Thesis To be submitted to the University of Warwick for the degree of Doctor of Philosophy in Physics Department of Physics January 2021 Contents Contents i List of Abbreviations iii List of Figures viii List of Tables xvii Acknowledgments xix Declarations xx Research Training xxv Abstract xxvi Preface xxvii I Initial Motivation......................................... xxvii II Thesis Structure.......................................... xxvii III Connections............................................ xxix Chapter 1 Review: Deep Learning in Electron Microscopy1 1.1 Scientific Paper..........................................1 1.2 Reflection............................................. 100 Chapter 2 Warwick Electron Microscopy Datasets 101 2.1 Scientific Paper.......................................... 101 2.2 Amendments and Corrections................................... 133 2.3 Reflection............................................. 133 Chapter 3 Adaptive Learning Rate Clipping Stabilizes Learning 136 3.1 Scientific Paper.......................................... 136 3.2 Amendments and Corrections................................... 147 3.3 Reflection............................................. 147 Chapter 4 Partial Scanning Transmission Electron Microscopy with Deep Learning 149 4.1 Scientific Paper.......................................... 149 4.2 Amendments and Corrections................................... 176 4.3 Reflection............................................. 176
    [Show full text]
  • Master Thesis
    Master thesis To obtain a Master of Science Degree in Informatics and Communication Systems from the Merseburg University of Applied Sciences Subject: Tunisian truck license plate recognition using an Android Application based on Machine Learning as a detection tool Author: Supervisor: Achraf Boussaada Prof.Dr.-Ing. Rüdiger Klein Matr.-Nr.: 23542 Prof.Dr. Uwe Schröter Table of contents Chapter 1: Introduction ................................................................................................................................. 1 1.1 General Introduction: ................................................................................................................................... 1 1.2 Problem formulation: ................................................................................................................................... 1 1.3 Objective of Study: ........................................................................................................................................ 4 Chapter 2: Analysis ........................................................................................................................................ 4 2.1 Methodological approaches: ........................................................................................................................ 4 2.1.1 Actual approach: ................................................................................................................................... 4 2.1.2 Image Processing with OCR: ................................................................................................................
    [Show full text]
  • Curriculum Vitae
    Diogo Castro Curriculum Vitae Summary I’m a Software Engineer based in Belfast, United Kingdom. My professional journey began as a C# developer, but Functional Programming (FP) soon piqued my interest and led me to learn Scala, Haskell, PureScript and even a bit of Idris. I love building robust, reliable and maintainable applications. I also like teaching and I’m a big believer in "paying it forward". I’ve learned so much from many inspiring people, so I make it a point to share what I’ve learned with others. To that end, I’m currently in charge of training new team members in Scala and FP, do occasional presentations at work and aim to do more public speaking. I blog about FP and Haskell at https://diogocastro.com/blog. Experience Nov 2017-Present Principal Software Engineer, SpotX, Belfast, UK. Senior Software Engineer, SpotX, Belfast, UK. Developed RESTful web services in Scala, using the cats/cats-effect framework and Akka HTTP. Used Apache Kafka for publishing of events, and Prometheus/Grafana for monitor- ing. Worked on a service that aimed to augment Apache Druid, a timeseries database, with features such as access control, a safer and simpler query DSL, and automatic conversion of monetary metrics to multiple currencies. Authored a Scala library for calculating the delta of any two values of a given type using Shapeless, a library for generic programming. Taught a weekly internal Scala/FP course, with the goal of preparing our engineers to be productive in Scala whilst building an intuition of how to program with functions and equational reasoning.
    [Show full text]
  • KNIME Deep Learning Integration Installation Guide
    KNIME Deep Learning Integration Installation Guide KNIME AG, Zurich, Switzerland Version 3.7 (last updated on 2019-02-06) Table of Contents Introduction. 1 KNIME Deep Learning Integrations . 1 KNIME Keras Integration Installation. 2 Python Installation. 2 Installing the KNIME Keras Integration. 3 Extensions . 3 GPU Support. 4 KNIME TensorFlow Integration Installation . 4 Installation . 4 Advanced . 4 GPU Support. 4 KNIME Deeplearning4j Installation. 5 Installation . 5 GPU Support. 5 Known Issues . 5 KNIME Deep Learning Integration Installation Guide Introduction This document describes how to install the KNIME Deep Learning Integrations. These integrations bring deep learning capabilities to KNIME Analytics Platform, which allow you to read, create, edit, train, and execute deep neural networks within KNIME Analytics Platform. KNIME Deep Learning Integrations Three different deep learning libraries have been integrated: KNIME Keras Integration The KNIME Keras Integration utilizes the Keras deep learning framework to enable users to read, write, train, and execute Keras deep learning networks within KNIME. Furthermore, you can also build custom deep learning networks directly in KNIME via the Keras layer nodes. KNIME Tensor Flow Integration The KNIME TensorFlow Integration provides access to the powerful machine learning library TensorFlow* within KNIME. It enables you to read, write, train, and execute TensorFlow networks directly in KNIME. You can also convert your Keras networks to TensorFlow networks with this extension for even greater flexibility. * TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. KNIME Deeplearning4j Integration The KNIME Deeplearning4j Integration integrates the Deeplearning4j library into KNIME, which provides deep learning capabilities in Java. Within KNIME this means you can read, write, train, execute, and build Deeplearning4j networks.
    [Show full text]
  • Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
    Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices 1st Chunjie Luo 2nd Xiwen He 3nd Jianfeng Zhan Institute of Computing Technology Institute of Computing Technology Institute of Computing Technology Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences BenchCouncil BenchCouncil BenchCouncil Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] 4nd Lei Wang 5nd Wanling Gao 6nd Jiahui Dai Institute of Computing Technology Institute of Computing Technology Beijing Academy of Frontier Chinese Academy of Sciences Chinese Academy of Sciences Sciences and Technology BenchCouncil BenchCouncil Beijing, China Beijing, China Beijing, China [email protected] wanglei [email protected] [email protected] Abstract—Due to increasing amounts of data and compute inference on the edge devices can 1) reduce the latency, 2) resources, deep learning achieves many successes in various protect the privacy, 3) consume the power [2]. domains. The application of deep learning on the mobile and em- To make the inference on the edge devices more efficient, bedded devices is taken more and more attentions, benchmarking and ranking the AI abilities of mobile and embedded devices the neural networks are designed more light-weight by using becomes an urgent problem to be solved. Considering the model simpler architecture, or by quantizing, pruning and compress- diversity and framework diversity, we propose a benchmark suite, ing the networks. Different networks present different trade- AIoTBench, which focuses on the evaluation of the inference offs between accuracy and computational complexity. These abilities of mobile and embedded devices.
    [Show full text]
  • See License Notices
    Open Source Components License Notices Last updated December 22, 2020 @nebular/eva-icons @ngx-translate/core @ngx-translate/http-loader libunwind dropbear 2017.75 popt MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice (including the next paragraph) shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --------------------------------------------------------------------------------------------------------------------- @angular/animations @angular/common @angular/core @angular/forms @angular/platform-browser @angular/router (v.1_11-19-20) The MIT License Copyright (c) 2010-2020 Google LLC. http://angular.io/license Permission
    [Show full text]
  • Implementing a System Architecture for Data and Multimedia Transmission in a Multi-UAV System Borey Uk, David Konam, Clément Passot, Milan Erdelj, Enrico Natalizio
    Implementing a System Architecture for Data and Multimedia Transmission in a Multi-UAV System Borey Uk, David Konam, Clément Passot, Milan Erdelj, Enrico Natalizio To cite this version: Borey Uk, David Konam, Clément Passot, Milan Erdelj, Enrico Natalizio. Implementing a System Architecture for Data and Multimedia Transmission in a Multi-UAV System. 16th International Conference on Wired/Wireless Internet Communications (IFIP WWIC 2018), Jun 2018, Boston, MA, United States. pp.246-257, 10.1007/978-3-030-02931-9_20. hal-02269736 HAL Id: hal-02269736 https://hal.inria.fr/hal-02269736 Submitted on 23 Aug 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Implementing a system architecture for data and multimedia transmission in a multi-UAV system Borey Uk, David Konam, Clément Passot, Milan Erdelj, and Enrico Natalizio Sorbonne Universités Université de Technologie de Compiègne UMR CNRS 7253 Heudiasyc, France {borey.uk, david.konam, clement.passot}@etu.utc.fr {milan.erdelj, enrico.natalizio}@hds.utc.fr Abstract. The development of Unmanned Aerial Vehicles (UAV) along with the ubiquity of Internet of Things (IoT) enables the creation of sys- tems that can provide real-time multimedia and data streaming.
    [Show full text]
  • Towards Left Duff S Mdbg Holt Winters Gai Incl Tax Drupal Fapi Icici
    jimportneoneo_clienterrorentitynotfoundrelatedtonoeneo_j_sdn neo_j_traversalcyperneo_jclientpy_neo_neo_jneo_jphpgraphesrelsjshelltraverserwritebatchtransactioneventhandlerbatchinsertereverymangraphenedbgraphdatabaseserviceneo_j_communityjconfigurationjserverstartnodenotintransactionexceptionrest_graphdbneographytransactionfailureexceptionrelationshipentityneo_j_ogmsdnwrappingneoserverbootstrappergraphrepositoryneo_j_graphdbnodeentityembeddedgraphdatabaseneo_jtemplate neo_j_spatialcypher_neo_jneo_j_cyphercypher_querynoe_jcypherneo_jrestclientpy_neoallshortestpathscypher_querieslinkuriousneoclipseexecutionresultbatch_importerwebadmingraphdatabasetimetreegraphawarerelatedtoviacypherqueryrecorelationshiptypespringrestgraphdatabaseflockdbneomodelneo_j_rbshortpathpersistable withindistancegraphdbneo_jneo_j_webadminmiddle_ground_betweenanormcypher materialised handaling hinted finds_nothingbulbsbulbflowrexprorexster cayleygremlintitandborient_dbaurelius tinkerpoptitan_cassandratitan_graph_dbtitan_graphorientdbtitan rexter enough_ram arangotinkerpop_gremlinpyorientlinkset arangodb_graphfoxxodocumentarangodborientjssails_orientdborientgraphexectedbaasbox spark_javarddrddsunpersist asigned aql fetchplanoriento bsonobjectpyspark_rddrddmatrixfactorizationmodelresultiterablemlibpushdownlineage transforamtionspark_rddpairrddreducebykeymappartitionstakeorderedrowmatrixpair_rddblockmanagerlinearregressionwithsgddstreamsencouter fieldtypes spark_dataframejavarddgroupbykeyorg_apache_spark_rddlabeledpointdatabricksaggregatebykeyjavasparkcontextsaveastextfilejavapairdstreamcombinebykeysparkcontext_textfilejavadstreammappartitionswithindexupdatestatebykeyreducebykeyandwindowrepartitioning
    [Show full text]
  • Running the Zephyr RTOS and Tensorflow Lite on RISC-V
    Running the Zephyr RTOS and TensorFlow Lite on RISC-V RISC-V Summit, Santa Clara, Dec 03, 2018 Michael Gielda, Antmicro, [email protected] Piotr Zierhoffer, Antmicro, [email protected] Pete Warden, Google, [email protected] ABOUT ZEPHYR WHAT IS THE ZEPHYR PROJECT? “The Zephyr™ Project is a Linux Foundation hosted Collaboration Project, (...) aiming to build a best-in-breed small, scalable, real-time operating system (RTOS) optimized for resource constrained devices, across multiple architectures.” WHAT IS THE ZEPHYR PROJECT? “The Zephyr™ Project is a Linux Foundation hosted Collaboration Project, (...) aiming to build a best-in-breed small, scalable, real-time operating system (RTOS) optimized for resource constrained devices, across multiple architectures.” WHY BOTHER WITH TINY CHIPS? I’m convinced that machine learning can run on tiny, low-power chips, and that this combination will solve a massive number of problems we have no solutions for right now. Pete Warden, Google's TensorFlow Mobile Technical Lead https://petewarden.com/2018/06/11/why-the-future-of-machine-learning-is-tiny/ Open Source, RTOS, Connected, Embedded Zephyr Project Fits where Linux is too big • Open source real time operating system • Vibrant Community participation • Built with safety and security in mind • Cross-architecture with growing Zephyr OS developer tool support 3rd Party Libraries • Vendor Neutral governance Application Services • Permissively licensed - Apache 2.0 OS Services • Complete, fully integrated, highly Kernel configurable, modular for flexibility, better than roll-your-own HAL • Product development ready with LTS • Certification ready with Auditable 6 OTHER REASONS WE NEED ZEPHYR • targeted at IoT and making it truly vendor-neutral & open source - BlueTooth, OpenThread..
    [Show full text]