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Titre : Open scientific data : why choosing and reusing the RIGHT DATA matters Type de document : Monographie Auteurs : Vera J. Lipton, Auteur Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 232 p. ISBN/ISSN/EAN : 978-1-83880-986-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes IGN] chercheur
[Termes IGN] Conseil européen pour la recherche nucléaire
[Termes IGN] données ouvertes
[Termes IGN] recherche scientifique
[Termes IGN] traitement de donnéesRésumé : (Editeur) This book shows how the vision for open access to scientific data can be more readily achieved through a staged model that research funders, policy makers, scientists, and research organizations can adopt in their practice. Drawing on her own experiences with data processing, on early findings with open scientific data at CERN (the European Organization for Nuclear Research), and from case studies of shared clinical trial data, the author updates our understanding of research data - what it is; how it dynamically evolves across different scientific disciplines and across various stages of research practice; and how it can, and indeed should, be shared at any of those stages. The result is a flexible and pragmatic path for implementing open scientific data. Note de contenu :
Chapter 1. Introduction: Opening Up Data in Scientific Research
Chapter 2. The Case for Open Scientific Data: Theory, Benefits, Costs and Opportunities
Chapter 3. The Current Policies of Research Funders and Publishers
Chapter 4. The Unclear Meaning of Open Scientific Data
Chapter 5. Research Data Management at CERN
Chapter 6. Open Sharing of Clinical Trial Data
Chapter 7. Legal Issues Arising in Open Scientific Data
Chapter 8. The Staged Model for Open Scientific Data
Chapter 9. Conclusion: Towards Achievable and Sustainable Open Scientific DataNuméro de notice : 26517 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie DOI : 10.5772/intechopen.87201 En ligne : https://doi.org/10.5772/intechopen.87201 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97307
Titre : Optical Coherence Tomography and Its Non-medical Applications Type de document : Monographie Auteurs : Michael R. Wang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 226 p. Format : 17 x 23 cm ISBN/ISSN/EAN : 978-1-83880-801-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chatoiement
[Termes IGN] contraste de couleurs
[Termes IGN] déformation d'image
[Termes IGN] empreinte
[Termes IGN] image 3D
[Termes IGN] image à haute résolution
[Termes IGN] métrologie
[Termes IGN] tomographieRésumé : (éditeur) Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications. Note de contenu : 1- Dynamic Range Enhancement in Swept-Source Optical Coherence Tomography
2- Multi-Frame Super resolution Optical Coherence Tomography for High Lateral Resolution 3D Imaging
3- OCT in Applications That Involve the Measurement of Large Dimensions
4- Low Cost Open-Source OCT Using Undergraduate Lab Components
5- Optical Coherence Tomography for Polymer Film Evaluation
6- Fouling Monitoring in Membrane Filtration Systems
7- Non destructive Characterization of Drying Processes of Colloidal Droplets and Latex Coats Using Optical Coherence Tomography
8- OCT for Examination of Cultural Heritage Objects
9- Quantitative Mapping of Strainsand Young Modulus Based on Phase-Sensitive OCT
10- OCT with a Visible Broadband Light Source Applied to High-Resolution Non destructive Inspection for Semiconductor Optical Devices
11- Optical Coherence Tomography for Non-Contact Evaluation of Fastener FlushnessNuméro de notice : 28530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.81767 En ligne : http://doi.org/10.5772/intechopen.81767 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97350
Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] qualité des eaux
[Termes IGN] signature spectrale
[Termes IGN] turbidité des eauxRésumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing
Chapter 1 - Hyperspectral endmember extraction techniques
Chapter 2 - Hyperspectral image classification
Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods
Chapter 4 - Fast chaotic encryption for hyperspectral images
Section Two - Applications of hyperspectral image processing
Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration
Chapter 6 - Use of hyperspectral remote sensing to estimate water qualityNuméro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243
Titre : Recent advances in image restoration with applications to real world problems Type de document : Monographie Auteurs : Chiman Kwan, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 ISBN/ISSN/EAN : 978-1-83968-356-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage non-dirigé
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction de modèle
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de terrain
[Termes IGN] reconstruction 3D
[Termes IGN] restauration d'imageRésumé : (Editeur) In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included. Note de contenu :
1. Introductory Chapter: Recent Advances in Image Restoration
2. Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data
3. Application of Deep Learning Approaches for Enhancing Mastcam Images
4. Generative Adversarial Networks for Visible to Infrared Video Conversion
5. Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution
6. Spatiotemporal Fusion in Remote Sensing
7. 3D Reconstruction through Fusion of Cross-View Images
8. Practical Digital Terrain Model Extraction Using Image Inpainting TechniquesNuméro de notice : 26695 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.90607 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.5772/intechopen.90607 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99081
Titre : Recent trends in artificial neural networks Type de document : Monographie Auteurs : Ali Sadollah, Éditeur scientifique ; Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 150 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-78985-859-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] logique floue
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy. This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance. Note de contenu : 1- Time series from clustering: An approach to forecast crime patterns
2- Encountered problems of time series with neural networks: Models and architectures
3- Metaheuristics and artificial neural networks
4- An improved algorithm for optimising the production of biochemical systems
5- Object recognition using convolutional neural networks
6- Prediction of wave energy potential in India: A fuzzy-ANN approach
7- Deep learning training and benchmarks for Earth observation images: Data sets, features, and procedures
8- Data mining technology for structural control systems: Concept, development, and comparisonNuméro de notice : 28497 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77409 En ligne : https://doi.org/10.5772/intechopen.77409 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99247 Geographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)
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