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[null,"1.","Record Nr.",null,"UNINA990001021460403321"]
[null,null,"Autore",null,"Langmuir, David B."]
[null,null,"Titolo",null,"Foundations of Future Electronics / A.R. von Hippel ... [et al.] ; edited by David B. Langmuir, W.D. Hershberger"]
[null,null,"Pubbl/distr/stampa",null,"New York : McGraw-Hill, 1961"]
[null,null,"Disciplina",null,"537.5"]
[null,null,"Locazione",null,"FI1"]
[null,null,"Collocazione",null,"31-008"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910464569303321"]
[null,null,"Titolo",null,"Advanced propulsion systems and technologies, today to 2020 [[electronic resource] /] / edited by Claudio Bruno, Antonio G. Accettura"]
[null,null,"Pubbl/distr/stampa",null,"Reston, Va., : American Institute of Aeronautics and Astronautics, c2008"]
[null,null,"ISBN",null,"1-60086-693-X"]
[{"$nulls":4},"1-60086-474-0"]
[null,null,"Descrizione fisica",null,"1 online resource (506 p.)"]
[null,null,"Collana",null,"Progress in astronautics and aeronautics ; ; v. 223"]
[null,null,"Disciplina",null,"629.475"]
[null,null,"Soggetti",null,"Space vehicles - Propulsion systems"]
[{"$nulls":4},"Propulsion systems"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"Commissioned by the European Space Agency\"--P. [4] of cover."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction -- Advanced solid rocket motors -- Advanced cryogenic engines -- Advanced LOX-HC engines for boosters and upper stages -- LOX-hydrocarbon engines in Russia -- Green propellants -- Green propellants in Russia -- Miniaturized propulsion -- Solar thermal propulsion for upper stages -- Electric-propulsion systems -- Superconductivity -- The case for nuclear propulsion : the Rubbia's engine -- VASIMR prefeasibility analysis -- Laser propulsion systems -- Mass accelerators : Maglev and Railguns -- Solar sails : propellantless propulsion for near- and medium-term deep-space missions -- In situ resource utilization."]
[null,"3.","Record Nr.",null,"UNINA9910639985103321"]
[null,null,"Autore",null,"Lyu Zhihan <1984-, >"]
[null,null,"Titolo",null,"Augmented Reality, Virtual Reality & Semantic 3D Reconstruction"]
[null,null,"Pubbl/distr/stampa",null,"Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022"]
[null,null,"ISBN",null,"3-0365-6062-9"]
[null,null,"Descrizione fisica",null,"1 electronic resource (304 p.)"]
[null,null,"Soggetti",null,"Technology: general issues"]
[{"$nulls":4},"History of engineering & technology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Augmented reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. In practical terms, this innovation can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Semantic 3D reconstruction unlocks the promise of AR technology, possessing a far greater availability of semantic information. Although, there are several methods currently available as post-processing approaches to extract semantic information from the reconstructed 3D models, the results obtained results have been uncertain and evenly incorrect. Thus, it is necessary to explore or develop a novel 3D reconstruction approach to automatically recover 3D geometry model and obtained semantic information simultaneously. The rapid advent of deep learning brought new opportunities to the field of semantic 3D reconstruction from photo collections. Deep learning-based methods are not only able to extract semantic information but can also enhance fundamental techniques in semantic 3D reconstruction, techniques which include feature matching or tracking, stereo matching, camera pose estimation, and use of multi-view stereo methods. Moreover, deep learning techniques can be used to extract priors from photo collections, and this obtained information can in turn improve the quality of 3D reconstruction."]