Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Stuart J. Gibson"'
Publikováno v:
Neurocomputing. 406:169-181
Recent work showed neural-network based approaches to reconstructing images from compressively sensed measurements offer significant improvements in accuracy and signal compression. Such methods can dramatically boost the capability of computational
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4317bf80d1f7607ab709f1c565a8a9c3
https://doi.org/10.1016/j.neucom.2020.04.010
https://doi.org/10.1016/j.neucom.2020.04.010
We present a theoretical study of the potential of Principal Component Analysis to analyse magnetic diffuse neutron scattering data on quantum materials. To address this question, we simulate the scattering function $S\left(\mathbf{q}\right)$ for a m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::672f36422ea7b52db15ab66ecf6c3874
Publikováno v:
Chemometrics and Intelligent Laboratory Systems. 184:175-181
Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identifying substances from noisy spectra without the n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffc70323c751a2f3b15bd5f6bfb34fb7
https://doi.org/10.1016/j.chemolab.2018.12.005
https://doi.org/10.1016/j.chemolab.2018.12.005
Autor:
Silvia Ramos, Dylan S Barker, Gunnar Möller, Sean Giblin, Stuart J. Gibson, Emma E. McCabe, Jorge Quintanilla, Adrian D. Hillier, Tymoteusz Tula
Publikováno v:
Journal of physics : condensed matter, 2021, Vol.33(19), pp.194002 [Peer Reviewed Journal]
In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as a tool to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b57820a6cb2304bf4bd0fdf8c05d1ae
https://kar.kent.ac.uk/85905/11/Tula_2021_J._Phys.__Condens._Matter_33_194002.pdf
https://kar.kent.ac.uk/85905/11/Tula_2021_J._Phys.__Condens._Matter_33_194002.pdf
Autor:
Christopher J. Solomon, Michael Foster, Lorna Ashton, Margarita Osadchy, Jinchao Liu, Stuart J. Gibson
Publikováno v:
The Analyst. 142:4067-4074
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an es
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4be9a2b0590ed6396812b162703e72d3
https://doi.org/10.1039/c7an01371j
https://doi.org/10.1039/c7an01371j
Autor:
C. Figura, Karl M. Menten, Friedrich Wyrowski, Toby J. T. Moore, Timea Csengeri, Thushara Pillai, Andrea Giannetti, Stuart J. Gibson, W.-J. Kim, Silvia Leurini, M. Wienen, Mark Thompson, James Urquhart
Publikováno v:
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2019, 484 (4), pp.4444-4470. ⟨10.1093/mnras/stz154⟩
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2019, 484 (4), pp.4444-4470. ⟨10.1093/mnras/stz154⟩
We have conducted a 3-mm molecular-line survey towards 570 high-mass star-forming clumps, using the Mopra telescope. The sample is selected from the 10,000 clumps identified by the ATLASGAL survey and includes all of the most important embedded evolu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33cbaa494eca454e88e14deab6cb6dc4
https://hal.archives-ouvertes.fr/hal-03054121
https://hal.archives-ouvertes.fr/hal-03054121
Autor:
Gilson A. Giraldi, Gilka Jorge Figaro Gattás, Stuart J. Gibson, Carlos Eduardo Thomaz, Daniel Rueckert, Igor R.R. Xavier
Publikováno v:
Biblioteca Digital de Teses e Dissertações da FEI
Centro Universitário da Fundação Educacional Inaciana (FEI)
instacron:FEI
Centro Universitário da Fundação Educacional Inaciana (FEI)
instacron:FEI
A number of studies have been developed recently in order to explore associations between craniofacial differences and genetics. Most of these works have been based on spatial face image models, adjusted for the counter effects of age. This approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56b388fb36d7d34c2c6496a308bd861d
https://repositorio.fei.edu.br/handle/FEI/1281
https://repositorio.fei.edu.br/handle/FEI/1281
Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac5b279b187a110f0e7b486f57c19ac5
Publikováno v:
Journal of Forensic Practice. 17:307-318
Purpose– The purpose of this paper is to describe four experiments evaluating post-production enhancement techniques with facial composites mainly created using the EFIT-V holistic system.Design/methodology/approach– Experiments 1-4 were conducte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab90fb94e7a0e861e5c20736f60ab27d
https://doi.org/10.1108/jfp-08-2015-0042
https://doi.org/10.1108/jfp-08-2015-0042
Autor:
David Manuel Arenas González, Stuart J. Gibson, Ana Lucila Sandoval Orozco, Luis Javier García Villalba, Julio C. Hernandez-Castro, Jocelin Rosales Corripio
Publikováno v:
IET Computer Vision. 9:723-731
The forensic analysis of digital images from mobile devices is particularly important given their quick expansion and everyday use in the society. A further consequence of digital images' widespread use is that they are used today as silent witnesses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bf506b367451ba48129ae615a86c892
https://doi.org/10.1049/iet-cvi.2014.0243
https://doi.org/10.1049/iet-cvi.2014.0243