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Articles by Serrano Gutiérrez
CONTROLLING DOPING PROFILES OF SILICON NANOWIRES FOR QUANTUM COMPUTING AND PHOTOVOLTAICS USING MICRO-RAMAN SPECTROSCOPY
Título CONTROLLING DOPING PROFILES OF SILICON NANOWIRES FOR QUANTUM COMPUTING AND PHOTOVOLTAICS USING MICRO-RAMAN SPECTROSCOPY Congreso Nanoscience Summer School @ Yachay 2023 Editorial Descripción Documento Fuente Nanoscience Summer School @ Yachay 2023, Puerto Ayora, Galapagos 23th-29th April 2023 Resumen Palabras Clave qubits, semiconductor, silicon, doping, Raman Patrocinador AEI MICINN TED - MTED NextGeneration Tipo de versión info:eu-repo/semantics/publishedVersion
Technology Development for Exploring Novel Concepts in Semiconductor Qubits
Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/66796 Título Technology Development for Exploring Novel Concepts in Semiconductor Qubits Congreso 14th Spanish Conference on Electron Devices (CDE) Descripción Documento Fuente 14th Spanish Conference on Electron Devices (CDE), Valencia, Spain. Resumen Palabras Clave Tipo de versión info:eu-repo/semantics/submittedVersion Formato: Adobe PDF Descripción: preprint file
Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision
Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/66613 Título Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision Documento Fuente arXiv:2403.05694 Resumen Patrocinador MCINN NextGeneration FEDER MTED AEI Tipo de versión info:eu-repo/semantics/submittedVersion Formato: Adobe PDF Descripción: Preprint manuscript with figures, tables, and references
Scipion Flexibility Hub: an integrative framework for advanced analysis of conformational heterogeneity in cryoEM
2.2.1. cryoDRGN cryoDRGN (Zhong et al., 2021) is a new algorithm for the study of macromolecular flexibility based on a neural network specifically designed and trained to learn how to reconstruct cryoEM maps from a given set of particles. These particles should have a previously estimated rigid alignment and CTF information, which will be used by the network to learn how to decode the requested reconstructions. The neural network design follows an autoencoder architecture.
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