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A comparative user study of visualization techniques for cluster analysis of multidimensional data sets / Elio Ventocilla in Information visualization, vol 19 n° 4 (October 2020)  

Public [article]  inInformation visualization > vol 19 n° 4 (October 2020) . - pp 318 - 338 Titre : A comparative user study of visualization techniques for cluster analysis of multidimensional data sets Type de document : Article/Communication Auteurs : Elio Ventocilla, Auteur ; Maria Riveiro, Auteur Année de publication : 2020 Article en page(s) : pp 318 - 338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] données multidimensionnelles [Termes IGN] modèle logique de données [Termes IGN] projection [Termes IGN] utilisateur [Termes IGN] visualisation de données [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, k, embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of k as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with k values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of k that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability. Numéro de notice : A2020-846 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177%2F1473871620922166 Date de publication en ligne : 04/07/2020 En ligne : https://doi.org/10.1177%2F1473871620922166 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98650 [article]

Unsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 61 - 72 Titre : Unsupervised change detection between SAR images based on hypergraphs Type de document : Article/Communication Auteurs : Jun Wang, Auteur ; Xuexi Yang, Auteur ; Xiangyu Yang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification non dirigée [Termes IGN] classification pixellaire [Termes IGN] corrélation automatique de points homologues [Termes IGN] détection de changement [Termes IGN] Hypergraph Based Data Structure [Termes IGN] image radar moirée [Termes IGN] partition des données [Termes IGN] précision de la classification Résumé : (auteur) The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection. Numéro de notice : A2020-251 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.007 Date de publication en ligne : 19/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94995 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020063	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020062	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Programming persistent memory / Steve Scargall (2020)  

Public Titre : Programming persistent memory : a comprehensive guide for developers Type de document : Guide/Manuel Auteurs : Steve Scargall, Auteur Editeur : New York : Apress Année de publication : 2020 Importance : 438 p. ISBN/ISSN/EAN : 978-1-4842-4932-1 Note générale : Glossaire Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] C++ [Termes IGN] données massives [Termes IGN] Java (langage de programmation) [Termes IGN] Linux [Termes IGN] modèle logique de données [Termes IGN] programmation informatique Résumé : (Auteur) Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK) Design and optimize data structures for persistent memory Study how real-world applications are modified to leverage persistent memory Utilize the tools available for persistent memory programming, application performance profiling, and debugging Who This Book Is For C, C++, Java, and Python developers, but will also be useful to software, cloud, and hardware architects across a broad spectrum of sectors, including cloud service providers, independent software vendors, high performance compute, artificial intelligence, data analytics, big data, etc. Note de contenu : Chapter 1: Introduction to Persistent Memory Programming Chapter 2: Persistent Memory Architecture Chapter 3: Operating System Support for Persistent Memory Chapter 4: Fundamental Concepts of Persistent Memory Programming Chapter 5: Introducing the Persistent Memory Development Kit Chapter 6: libpmem: Low-Level Persistent Memory Support Chapter 7: libpmemobj: A Native Transactional Object Store Chapter 8: libpmemobj-cpp: The Adaptable Language - C++ and Persistent Memory Chapter 9: pmemkv: A Persistent In-Memory Chapter 10: Volatile Use of Persistent Memory Chapter 11: Designing Data Structures for Persistent Memory Chapter 12: Debugging Persistent Memory Applications Chapter 13: Enabling Persistence Using a Real-World Application Chapter 14: Concurrency and Persistent Memory Chapter 15: Profiling and Performance Chapter 16: PMDK Internals: Important Algorithms and Data Structures Chapter 17: Reliability, Availability, and Serviceability (RAS) Chapter 18: Remote Persistent Memory Chapter 19: Advanced Topics Appendix A: How to Install NDCTL and DAXCTL on Linux Appendix B: How to Install the Persistent Memory Development Kit (PMDK) Appendix C: How to Install IPMCTL on Linux and Windows Appendix D: Java for Persistent Memory Appendix E: The Future of Remote Persistent Memory Replication Numéro de notice : 26513 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-1-4842-4932-1 En ligne : http://doi.org/10.1007/978-1-4842-4932-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97277

Pro TBB / Michael Voss (2019)  

Public Titre : Pro TBB : C++ parallel programming with Threading Building Blocks Type de document : Guide/Manuel Auteurs : Michael Voss, Auteur ; Rafael Asenjo, Auteur ; James Reinders, Auteur Editeur : New York : Apress Année de publication : 2019 Autre Editeur : Springer Nature Importance : 754 p. ISBN/ISSN/EAN : 978-1-4842-4398-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] C++ [Termes IGN] modèle logique de données [Termes IGN] programmation informatique [Termes IGN] Standard Template Library Résumé : (Editeur) This open access book is a modern guide for all C++ programmers to learn Threading Building Blocks (TBB). Written by TBB and parallel programming experts, this book reflects their collective decades of experience in developing and teaching parallel programming with TBB, offering their insights in an approachable manner. Throughout the book the authors present numerous examples and best practices to help you become an effective TBB programmer and leverage the power of parallel systems. Pro TBB starts with the basics, explaining parallel algorithms and C++'s built-in standard template library for parallelism. You'll learn the key concepts of managing memory, working with data structures and how to handle typical issues with synchronization. Later chapters apply these ideas to complex systems to explain performance tradeoffs, mapping common parallel patterns, controlling threads and overhead, and extending TBB to program heterogeneous systems or system-on-chips. What You'll Learn Use Threading Building Blocks to produce code that is portable, simple, scalable, and more understandable Review best practices for parallelizing computationally intensive tasks in your applications Integrate TBB with other threading packages Create scalable, high performance data-parallel programs Work with generic programming to write efficient algorithms Who This Book Is For C++ programmers learning to run applications on multicore systems, as well as C or C++ programmers without much experience with templates. No previous experience with parallel programming or multicore processors is required. Note de contenu : PART 1 - Jumping Right In: “Hello, TBB!” - Generic Parallel Algorithms - Flow Graphs - TBB and the Parallel Algorithms of the C++ Standard Template Library - Synchronization: Why and How to Avoid It - Data Structures for Concurrency - Scalable Memory Allocation - Mapping Parallel Patterns to TBB PART 2 - The Pillars of Composability - Using Tasks to Create Your Own Algorithms - Controlling the Number of Threads Used for Execution - Using Work Isolation for Correctness and Performance - Creating Thread-to-Core and Task-to-Thread Affinity - Using Task Priorities - Cancellation and Exception Handling - Tuning TBB Algorithms: Granularity, Locality, Parallelism, and Determinism - Flow Graphs: Beyond the Basics - Beef Up Flow Graphs with Async Nodes - Flow Graphs on Steroids: OpenCL Nodes - TBB on NUMA Architectures Numéro de notice : 26521 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-1-4842-4398-5 En ligne : http://doi.org/10.1007/978-1-4842-4398-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97474

The map as knowledge base / Dalia E. Varanka in International journal of cartography, vol 4 n° 2 (June 2018)  

Public [article]  inInternational journal of cartography > vol 4 n° 2 (June 2018) . - pp 201 - 223 Titre : The map as knowledge base Type de document : Article/Communication Auteurs : Dalia E. Varanka, Auteur ; E. Lynn Usery, Auteur Année de publication : 2018 Article en page(s) : pp 201 - 223 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] base de connaissances [Termes IGN] carte [Termes IGN] interface utilisateur [Termes IGN] modèle logique de données [Termes IGN] PostgreSQL [Termes IGN] SPARQL [Termes IGN] visualisation cartographique [Termes IGN] web sémantique Résumé : (Auteur) This paper examines the concept and implementation of a map as a knowledge base. A map as a knowledge base means that the visual map is not only the descriptive compilation of data and design principles, but also involves a compilation of semantic propositions and logical predicates that create a body of knowledge organized as a map. The digital product of a map as knowledge base can be interpreted by machines, as well as humans, and can provide access to the knowledge base through interfaces to select features and other information from the map. The design of maps as a knowledge base involves technical approaches and a system architecture to support a knowledge base. This paper clarifies how a map as a knowledge base differs from earlier map theory models by investigating the knowledge-based concepts of implementation through logical modelling, a knowledge repository, user interfaces for information access, and cartographic visualization. The paper ends with proof of concepts for two types of cartographic data query. Numéro de notice : A2018-426 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2017.1421004 Date de publication en ligne : 20/05/2018 En ligne : https://doi.org/10.1080/23729333.2017.1421004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90919 [article]

Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Efficient terrestrial laser scan segmentation exploiting data structure / Hamid Mahmoudabadi in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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3D building reconstruction from ALS data using unambiguous decomposition into elementary structures / Malgorzata Jarząbek-Rychard in ISPRS Journal of photogrammetry and remote sensing, vol 118 (August 2016)  

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A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

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BIM-GIS, une convergence annoncée / Anonyme in Géomatique expert, n° 109 (mars - avril 2016)

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Méthode pour la reconstruction, l'analyse et l'exploitation de réseaux tridimensionnels en milieu urbain / Marie Lacroix (2016)  

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Querying visible points in large obstructed space / Jianqiu Xu in Geoinformatica, vol 19 n° 3 (July - September 2015)  

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An evaluation and classification of nD topological data structures for the representation of objects in a higher-dimensional GIS / Ken Arroyo Ohori in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)  

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Data structures and algorithms with Python / Kent D. Lee (2015)  

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Abstracting geographic information in a data rich world / Dirk Burghardt (2014)

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