Literatura académica sobre el tema "Spectral inversion"
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Artículos de revistas sobre el tema "Spectral inversion"
Riethmüller, T. L. y S. K. Solanki. "The potential of many-line inversions of photospheric spectropolarimetric data in the visible and near UV". Astronomy & Astrophysics 622 (24 de enero de 2019): A36. http://dx.doi.org/10.1051/0004-6361/201833379.
Texto completoHall, R. L. "Geometric spectral inversion". Journal of Physics A: Mathematical and General 28, n.º 6 (23 de marzo de 1995): 1771–86. http://dx.doi.org/10.1088/0305-4470/28/6/028.
Texto completoRubino, J. Germán y Danilo Velis. "Thin-bed prestack spectral inversion". GEOPHYSICS 74, n.º 4 (julio de 2009): R49—R57. http://dx.doi.org/10.1190/1.3148002.
Texto completoHofmann, Ryan A., Kevin P. Reardon, Ivan Milic, Momchil E. Molnar, Yi Chai y Han Uitenbroek. "Evaluating Non-LTE Spectral Inversions with ALMA and IBIS". Astrophysical Journal 933, n.º 2 (1 de julio de 2022): 244. http://dx.doi.org/10.3847/1538-4357/ac6f00.
Texto completoMuyzert, Everhard. "Seabed property estimation from ambient-noise recordings: Part 2 — Scholte-wave spectral-ratio inversion". GEOPHYSICS 72, n.º 4 (julio de 2007): U47—U53. http://dx.doi.org/10.1190/1.2719062.
Texto completoQi, Haixia, Bingyu Zhu, Lingxi Kong, Weiguang Yang, Jun Zou, Yubin Lan y Lei Zhang. "Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves". Applied Sciences 10, n.º 7 (26 de marzo de 2020): 2259. http://dx.doi.org/10.3390/app10072259.
Texto completoXue, Yun, Bin Zou, Yimin Wen, Yulong Tu y Liwei Xiong. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra". Sustainability 12, n.º 11 (29 de mayo de 2020): 4441. http://dx.doi.org/10.3390/su12114441.
Texto completoWang, Weiyan, Yungui Zhang, Zhihong Li, Qingli Liu, Wenqiang Feng, Yulan Chen, Hong Jiang, Hui Liang y Naijie Chang. "Fourier-Transform Infrared Spectral Inversion of Soil Available Potassium Content Based on Different Dimensionality Reduction Algorithms". Agronomy 13, n.º 3 (21 de febrero de 2023): 617. http://dx.doi.org/10.3390/agronomy13030617.
Texto completoNeukirch, Maik, Antonio García-Jerez, Antonio Villaseñor, Francisco Luzón, Jacques Brives y Laurent Stehly. "On the Utility of Horizontal-to-Vertical Spectral Ratios of Ambient Noise in Joint Inversion with Rayleigh Wave Dispersion Curves for the Large-N Maupasacq Experiment". Sensors 21, n.º 17 (4 de septiembre de 2021): 5946. http://dx.doi.org/10.3390/s21175946.
Texto completoCięszczyk, Sławomir. "A Multi-Band Integrated Virtual Calibration-Inversion Method for Open Path FTIR Spectrometry". Metrology and Measurement Systems 20, n.º 2 (1 de junio de 2013): 287–98. http://dx.doi.org/10.2478/mms-2013-0025.
Texto completoTesis sobre el tema "Spectral inversion"
Orozco, M. Catalina (Maria Catalina). "Inversion Method for Spectral Analysis of Surface Waves (SASW)". Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5124.
Texto completoSet, Sze Yun. "Dispersion compensation in high bit rate transmission systems using midspan spectral inversion". Thesis, University of Southampton, 1998. https://eprints.soton.ac.uk/394393/.
Texto completoDeng, Mo Ph D. Massachusetts Institute of Technology. "Deep learning with physical and power-spectral priors for robust image inversion". Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127013.
Texto completoCataloged from the official PDF of thesis.
Includes bibliographical references (pages 169-182).
Computational imaging is the class of imaging systems that utilizes inverse algorithms to recover unknown objects of interest from physical measurements. Deep learning has been used in computational imaging, typically in the supervised mode and in an End-to-End fashion. However, treating the machine learning algorithm as a mere black-box is not the most efficient, as the measurement formation process (a.k.a. the forward operator), which depends on the optical apparatus, is known to us. Therefore, it is inefficient to let the neural network to explain, at least partly, the system physics. Also, some prior knowledge of the class of objects of interest can be leveraged to make the training more efficient. The main theme of this thesis is to design more efficient deep learning algorithms with the help of physical and power-spectral priors.
We first propose the learning to synthesize by DNN (LS-DNN) scheme, where we propose a dual-channel DNN architecture, each designated to low and high frequency band, respectively, to split, process, and subsequently, learns to recombine low and high frequencies for better inverse conversion. Results show that the LS-DNN scheme largely improves reconstruction quality in many applications, especially in the most severely ill-posed case. In this application, we have implicitly incorporated the system physics through data pre-processing; and the power-spectral prior through the design of the band-splitting configuration. We then propose to use the Phase Extraction Neural Networks (PhENN) trained with perceptual loss, that is based on extracted feature maps from pre-trained classification neural networks, to tackle the problem of low-light phase retrieval under low-light conditions.
This essentially transfer the knowledge, or features relevant to classifications, and thus corresponding to human perceptual quality, to the image-transformation network (such as PhENN). We find that the commonly defined perceptual loss need to be refined for the low-light applications, to avoid the strengthened "grid-like" artifacts and achieve superior reconstruction quality. Moreover, we investigate empirically the interplay between the physical and con-tent prior in using deep learning for computational imaging. More specifically, we investigate the effect of training examples to the learning of the underlying physical map and find that using training datasets with higher Shannon entropy is more beneficial to guide the training to correspond better to the system physics and thus the trained mode generalizes better to test examples disjoint from the training set.
Conversely, if more restricted examples are used as training examples, the training can be guided to undesirably "remember" to produce the ones similar as those in training, making the cross-domain generalization problematic. Next, we also propose to use deep learning to greatly accelerate the optical diffraction tomography algorithm. Unlike previous algorithms that involve iterative optimization algorithms, we present significant progresses towards 3D refractive index (RI) maps from a single-shot angle-multiplexing interferogram. Last but not least, we propose to use cascaded neural networks to incorporate the system physics directly into the machine learning algorithms, while leaving the trainable architectures to learn to function as the ideal Proximal mapping associated with the efficient regularization of the data. We show that this unrolled scheme significantly outperforms the End-to-End scheme, in low-light imaging applications.
by Mo Deng.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Pappalardo, Cirino, Bernd Vollmer y Ariane Lancon. "The star formation history of Virgo spiral galaxies. Combined spectral and photometric inversion". Phd thesis, Université de Strasbourg, 2010. http://tel.archives-ouvertes.fr/tel-00483128.
Texto completoPappalardo, Cirino. "The star formation history of Virgo spiral galaxies : combined spectral and photometric inversion". Strasbourg, 2010. https://publication-theses.unistra.fr/public/theses_doctorat/2010/PAPPALARDO_Cirino_2010.pdf.
Texto completoThis thesis investigates the influence of ram pressure stripping on the star formation history of cluster spiral galaxies. Ram pressure stripping is the hydrodynamical interaction between the interstellar medium (ISM) of a spiral galaxy that is moving inside the potential well of a cluster, and the intracluster medium (ICM). If the dynamical pressure exerted by the ICM is larger than the restoring force due to the galactic potential, the galaxy loses gas from the outer disk. The Virgo cluster is an ideal laboratory to study environmental effects on galaxy evolution, because it is rich in spirals and dynamically young. From observations we know that the amount of atomic gas in Virgo spirals is less than that of galaxies in the field. In particular cluster spirals show truncated HI disks (Giovanelli & Haynes 1983, Cayatte et al. 1990). For those galaxies that also show a symmetrical stellar distribution, ram pressure stripping is the most probably origin of the gas-disk truncation
OLIVEIRA, OTAVIO KAMINSKI DE. "INVERSION OF NONLINEAR PERTURBATIONS OF THE LAPLACIAN IN GENERAL DOMAINS WITH FINITE SPECTRAL INTERACTION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27930@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Consideramos a análise numérica de perturbações não lineares do Laplaciano definido em regiões limitadas tratáveis pelo Método de Elementos Finitos. Supomos que as não linearidades interagem com k autovalores do Laplaciano livre. Apresentamos uma redução do problema à inversão de uma função de k variáveis e delineamos uma técnica para tal. O texto é uma extensão dos trabalhos de Cal Neto, Malta, Saldanha e Tomei.
We consider the numerical analysis of nonlinear perturbations of the Laplacian defined in limited regions amenable to the Finite Element Method. The nonlinearities are supposed to interact only with k eigenvalues of the free Laplacian. We present a reduction of the problem to the inversion of a function of k variables and indicate a technique to do so. The text extends the works by Cal Neto, Malta, Saldanha and Tomei.
Reine, Carl Andrew. "A robust prestack Q-Inversion in the T-p Domain using variable-window spectral estimates". Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511145.
Texto completoFernandez, Cesar Aaron Moya. "Two alternative inversion techniques for the determination of seismic site response and propagation-path velocity structure : spectral inversion with reference events and neural networks". 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/147831.
Texto completoMarkusson, Ola. "Model and System Inversion with Applications in Nonlinear System Identification and Control". Doctoral thesis, KTH, Signals, Sensors and Systems, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3287.
Texto completoGhorbani, Ahmad. "Contribution au développement de la résistivité complexe et à ses applications en environnement". Paris 6, 2007. http://www.theses.fr/2007PA066607.
Texto completoLibros sobre el tema "Spectral inversion"
Data inversion algorithm development for the hologen [i.e. halogen] occultation experiment. [Williamsburg, Va.?]: College of William and Mary, 1987.
Buscar texto completoMyers, Timothy F. Proposed implementation of a near-far resistant multiuser detector without matrix inversion using Delta-Sigma modulation. 1992.
Buscar texto completoBrown, Derek H. Projectivism and Phenomenal Presence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199666416.003.0010.
Texto completoCapítulos de libros sobre el tema "Spectral inversion"
Fichtner, Andreas. "Spectral-Element Methods". En Full Seismic Waveform Modelling and Inversion, 59–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15807-0_4.
Texto completoLeeb, H., H. Fiedeldey, R. Lipperheide y W. A. Schnizer. "Inversion of Three-Quark Spectral Data". En Inverse Problems and Theoretical Imaging, 356–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75298-8_44.
Texto completoKurtuluş, C. y M. Alpmen. "Spectral Analysis of Blast Vibrations from Large Explosions". En Theory and Practice of Geophysical Data Inversion, 283–308. Wiesbaden: Vieweg+Teubner Verlag, 1992. http://dx.doi.org/10.1007/978-3-322-89417-5_18.
Texto completoHori, Muneo. "Inversion Method Using Spectral Decomposition of Green’s Function". En IUTAM Symposium on Field Analyses for Determination of Material Parameters — Experimental and Numerical Aspects, 123–37. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0109-0_12.
Texto completoFiedeldey, H. "Inversion at Fixed-Energy for Nonlocal and Algebraic Potentials and N-Body Spectral Inversion". En Lecture Notes in Physics, 176–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-13969-1_12.
Texto completoFiedeldey, H. "Inversion at fixed-energy for nonlocal and algebraic potentials and N-body spectral inversion". En Lecture Notes in Physics, 176–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57576-6_12.
Texto completoMecozzi, Antonio. "Devices for all-optical wavelength conversion and spectral inversion". En Optical Networks: Design and Modelling, 25–29. New York, NY: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35398-2_3.
Texto completoSocas-Navarro, Héctor. "Non-LTE Inversion of Spectral Lines and Stokes Profiles". En Highlights of Spanish Astrophysics II, 233–40. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-017-1776-2_55.
Texto completoCuomo, V., U. Amato, R. Rizzi, C. Serio y V. Tramutoli. "Topics in Optimal Inversion Schemes Applied to Atmospheric Structure Retrieval". En High Spectral Resolution Infrared Remote Sensing for Earth’s Weather and Climate Studies, 163–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84599-4_11.
Texto completoHorvath, H., F. J. Olmo, L. Alados Arboledas, O. Jovanovic, M. Gangl, W. Kaller, C. Sanchez, H. Sauerzopf y S. Seidl. "Size Distributions of Particles Obtained by Inversion of Spectral Extinction and Scattering Measurements". En Optics of Cosmic Dust, 143–58. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0628-6_9.
Texto completoActas de conferencias sobre el tema "Spectral inversion"
Maulana, A. D. "Seismic Inversion Resolution Enhancement With (3S) Spectral Blueing, Spectral Balancing, and Stochastic Inversion on Fluvio Deltaic Environment". En Indonesian Petroleum Association - 46th Annual Convention & Exhibition 2022. Indonesian Petroleum Association, 2022. http://dx.doi.org/10.29118/ipa22-g-126.
Texto completoHALL, RICHARD L. "GEOMETRIC SPECTRAL INVERSION". En Proceedings of the 13th Regional Conference. World Scientific Publishing Company, 2012. http://dx.doi.org/10.1142/9789814417532_0001.
Texto completoSwiatlowski, J. y W. Leoński. "Short pulse-induced population inversion for continuum-continuum transitions". En Spectral line shapes. AIP, 1990. http://dx.doi.org/10.1063/1.39932.
Texto completoSharma, Anshuman, Vishnu Kishore Pai y N. Reviraj. "Spectral inversion in QPSK receiver". En 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955766.
Texto completoNing, Chengda, Xianyong Jing, Zhihuan Lan y Chunyan Tian. "Spectral measuring temperature inversion study". En 2016 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA-16). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/wartia-16.2016.76.
Texto completoZhou*, Donghong, Bo Wang, Zhanghong Shen y Gang Peng. "Geostatistical spectral inversion: The thin layer study using spectral inversion method with geostatistical information". En SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, 2014. http://dx.doi.org/10.1190/segam2014-0666.1.
Texto completoBoardman, Joseph W. "Inversion of high spectral resolution data". En Imaging Spectroscopy of the Terrestrial Environment, editado por Gregg Vane. SPIE, 1990. http://dx.doi.org/10.1117/12.21355.
Texto completoChen, Siyuan, Siyuan Cao, Yaoguang Sun y Yumeng Jiang. "Nonstationary spectral inversion of seismic data". En First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3583203.1.
Texto completoBonar, David C. y Mauricio D. Sacchi. "Complex spectral decomposition via inversion strategies". En SEG Technical Program Expanded Abstracts 2010. Society of Exploration Geophysicists, 2010. http://dx.doi.org/10.1190/1.3513105.
Texto completoLazaratos, Spyros y Roy L. David. "Inversion by pre‐migration spectral shaping". En SEG Technical Program Expanded Abstracts 2009. Society of Exploration Geophysicists, 2009. http://dx.doi.org/10.1190/1.3255338.
Texto completoInformes sobre el tema "Spectral inversion"
Yeh. Spectral Logic Inversion Using Optical Wave Mixing. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1995. http://dx.doi.org/10.21236/ada307495.
Texto completoWhite, H. P., J. C. Deguise, J. W. Schwarz, R. Hitchcock y K. Staenz. Defining Shaded Spectra by Model Inversion for Spectral Unmixing of Hyperspectral Datasets - Theory and Preliminary Application. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219895.
Texto completoBaumgardt, Douglas R. y Angelina Freeman. Characterization of Underwater Explosions by Spectral/Cepstral Analysis, Modeling and Inversion. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2005. http://dx.doi.org/10.21236/ada443931.
Texto completoDesbarats, A. J. An iterative least-square method for the inversion of spectral radiometric data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128069.
Texto completoGriem, H. Experimental study of population inversion and spectral line broadening in a plasma containing a mixture of high Z and low Z ions. Office of Scientific and Technical Information (OSTI), octubre de 1988. http://dx.doi.org/10.2172/7264387.
Texto completoAuthor, Not Given. Source Spectra Analysis of SPE Phase I from Frequency-Domain Moment Tensor Inversion. Office of Scientific and Technical Information (OSTI), noviembre de 2017. http://dx.doi.org/10.2172/1407858.
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