Literatura académica sobre el tema "Spectral mixtures"
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Artículos de revistas sobre el tema "Spectral mixtures"
Lo, Su-Chin y Chris W. Brown. "Infrared Spectral Search for Mixtures in Medium-Size Libraries". Applied Spectroscopy 45, n.º 10 (diciembre de 1991): 1621–27. http://dx.doi.org/10.1366/0003702914335256.
Texto completoDucasse, Etienne, Karine Adeline, Xavier Briottet, Audrey Hohmann, Anne Bourguignon y Gilles Grandjean. "Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods". Remote Sensing 12, n.º 11 (27 de mayo de 2020): 1723. http://dx.doi.org/10.3390/rs12111723.
Texto completoBrown, Chris W., Anne E. Okafor, Steven M. Donahue y Su-Chin Lo. "UV-Visible Spectral Library Search with Mixtures". Applied Spectroscopy 49, n.º 7 (julio de 1995): 1022–27. http://dx.doi.org/10.1366/0003702953964723.
Texto completoMagazù, S., E. Calabrò y M. T. Caccamo. "Experimental Study of Thermal Restraint in Bio-Protectant Disaccharides by FTIR Spectroscopy". Open Biotechnology Journal 12, n.º 1 (31 de julio de 2018): 123–33. http://dx.doi.org/10.2174/1874070701812010123.
Texto completoAnastasiadis, Johannes y Michael Heizmann. "GAN-regularized augmentation strategy for spectral datasets". tm - Technisches Messen 89, n.º 4 (5 de febrero de 2022): 278–88. http://dx.doi.org/10.1515/teme-2021-0109.
Texto completoGolyak, I. S., E. R. Kareva, I. L. Fufurin, D. R. Anfimov, A. V. Scherbakova, A. O. Nebritova, P. P. Demkin y A. N. Morozov. "Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath". Computer Optics 46, n.º 4 (agosto de 2022): 650–58. http://dx.doi.org/10.18287/2412-6179-co-1058.
Texto completoLo, Su-Chin y Chris W. Brown. "Infrared Spectral Search for Mixtures in Large-Size Libraries". Applied Spectroscopy 45, n.º 10 (diciembre de 1991): 1628–32. http://dx.doi.org/10.1366/0003702914335111.
Texto completoNyden, Marc R. y Krishnan Chittur. "Component Spectrum Reconstruction from Partially Characterized Mixtures". Applied Spectroscopy 43, n.º 1 (enero de 1989): 123–28. http://dx.doi.org/10.1366/0003702894201743.
Texto completoCiarniello, Mauro, Lyuba V. Moroz, Olivier Poch, Vassilissa Vinogradoff, Pierre Beck, Batiste Rousseau, Istiqomah Istiqomah et al. "VIS-IR Spectroscopy of Mixtures of Water Ice, Organic Matter, and Opaque Mineral in Support of Small Body Remote Sensing Observations". Minerals 11, n.º 11 (3 de noviembre de 2021): 1222. http://dx.doi.org/10.3390/min11111222.
Texto completoKulko, Roman-David, Alexander Pletl, Andreas Hanus y Benedikt Elser. "Detection of Plastic Granules and Their Mixtures". Sensors 23, n.º 7 (24 de marzo de 2023): 3441. http://dx.doi.org/10.3390/s23073441.
Texto completoTesis sobre el tema "Spectral mixtures"
Ajohani, Maha. "SPECTRAL PHASOR ANALYSIS ON ABSORBANCE SPECTRA FOR QUANTIFYING THE CONTENT OF DYE MIXTURES". Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1464191406.
Texto completoVlack, Yvette A. "A Diffuse Spectral Reflectance Library of Clay Minerals and Clay Mixtures within the VIS/NIR Bands". Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1227006436.
Texto completoLuo, Zhaohui. "GC/FT-ICR Mass Spectral Analysis of Complex Mixtures: A Multidimensional Approach for Online Gas Phase Basicity Measurements". Fogler Library, University of Maine, 2006. http://www.library.umaine.edu/theses/pdf/LuoZX2006.pdf.
Texto completoRaksuntorn, Nareenart. "Unsupervised spectral mixture analysis for hyperspectral imagery". Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-04192009-142516.
Texto completoLevi, Di Leon Rémi. "Etude théorique et expérimentale de l'absorption par CO2 et H2O dans le domaine infrarouge à température élevée". Châtenay-Malabry, Ecole centrale de Paris, 1986. http://www.theses.fr/1986ECAP0026.
Texto completoParra, Vásquez Gabriel Enrique. "Spectral mixture kernels for Multi-Output Gaussian processes". Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/150553.
Texto completoMulti-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression problem by modeling the correlation in time and/or space (as scalar GPs do), but also across channels and thus revealing statistical dependencies among different sources of data. This is crucial in a number of real-world applications such as fault detection, data imputation and financial time-series analysis. Analogously to the univariate case, MOGPs are entirely determined by a multivariate covariance function, which in this case is matrix valued. The design of this matrix-valued covariance function is challenging, since we have to deal with the trade off between (i) choosing a broad class of cross-covariances and auto-covariances, while at the same time (ii) ensuring positive definiteness of the symmetric matrix containing these scalar-valued covariance functions. In the stationary univariate case, these difficulties can be bypassed by virtue of Bochner's theorem, that is, by building the covariance function in the spectral (Fourier) domain to then transform it to the time and/or space domain, thus yielding the (single-output) Spectral Mixture kernel \cite{Wilson:2013}. A classical approach to define multivariate covariance functions for MOGPs is through linear combinations of independent (latent) GPs; this is the case of the Linear Model of Coregionalization (LMC \cite{goo1997}) and the Convolution Model \cite{Alvarez:2008}. In these cases, the resulting multivariate covariance function is a function of both the latent-GP covariances and the linear operator considered, which usually results in symmetric cross-covariances that do not admit lags across channels. Due to their simplicity, these approaches fail to provide interpretability of the dependencies learnt and force the auto-covariances to have similar structure. The main purpose of this work is to extend the spectral mixture concept to MOGPs: We rely on Cram\'er's theorem \cite, the multivariate version of Bochner's theorem, to propose an expressive family of complex-valued square-exponential cross-spectral densities, which, through the Fourier transform yields the Multi-Output Spectral Mixture kernel (MOSM). The proposed MOSM model provides clear interpretation of all the parameters in spectral terms. Besides the theoretical presentation and interpretation of the proposed multi-output covariance kernel based on square-exponential spectral densities, we inquiry the plausibility of complex-valued t-Student cross-spectral densities. We validate our contribution experimentally through an illustrative example using a tri-variate synthetic signal, and then compare it against all the aforementioned methods on two real-world datasets.
Stuttle, Matthew Nicholas. "A gaussian mixture model spectral representation for speech recognition". Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620077.
Texto completoRaman, Pujita. "Speaker Identification and Verification Using Line Spectral Frequencies". Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52964.
Texto completoMaster of Science
Gurden, Stephen P. "Deconvolution of vapour-phase mid-infrared mixture spectra of organic solvents using chemometrics". Thesis, University of Bristol, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337176.
Texto completoKressler, Florian. "The Integration of Remote Sensing and Ancillary Data". WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4256/1/WSG_RR_0896.pdf.
Texto completoSeries: Research Reports of the Institute for Economic Geography and GIScience
Libros sobre el tema "Spectral mixtures"
Hawaii Institute of Geophysics. Planetary Geosciences Division. y United States. National Aeronautics and Space Administration., eds. Spectral reflectance (0.4 - 5.0 [microns]) of sulfur related compounds and mixtures. Honolulu, Hawaii: Planetary Geosciences Division, Hawaii Institute of Geophysics, University of Hawaii, 1987.
Buscar texto completoHawaii Institute of Geophysics. Planetary Geosciences Division. y United States. National Aeronautics and Space Administration., eds. Spectral reflectance (0.4 - 5.0 [microns]) of sulfur related compounds and mixtures. Honolulu, Hawaii: Planetary Geosciences Division, Hawaii Institute of Geophysics, University of Hawaii, 1987.
Buscar texto completoShimabukuro, Yosio Edemir y Flávio Jorge Ponzoni. Spectral Mixture for Remote Sensing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02017-0.
Texto completoLin, Li, He Guoqi y United States. National Aeronautics and Space Administration., eds. Nonlinear spectral mixture modeling of lunar multispectral: Implications for lateral transport. [Washington, DC: National Aeronautics and Space Administration, 1997.
Buscar texto completoWehrmeyer, Joseph A. Temperature and mixture fraction profiles in counterflow diffusion flames using linewise Raman imaging. Washington, D. C: AIAA, 1995.
Buscar texto completoSo odd a mixture: Along the autistic spectrum in Pride and prejudice. London: Jessica Kingsley Publishers, 2007.
Buscar texto completoUnited States. National Aeronautics and Space Administration., ed. NONLINEAR SPECTRAL MIXTURE MODELING OF LUNAR MULTISPECTRAL: IMPLICATIONS FOR LATERAL TRANSPORT... NASA/CR-1998-208863... SEP. 10, 1999. [S.l: s.n., 2000.
Buscar texto completoRuckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging. Elsevier Science & Technology Books, 2016.
Buscar texto completoRuckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 2016.
Buscar texto completoResolving Spectral Mixtures - With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-00401-4.
Texto completoCapítulos de libros sobre el tema "Spectral mixtures"
Achlioptas, Dimitris y Frank McSherry. "On Spectral Learning of Mixtures of Distributions". En Learning Theory, 458–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_31.
Texto completoLuo, Bin y Sibao Chen. "LPP and LPP Mixtures for Graph Spectral Clustering". En Advances in Image and Video Technology, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_12.
Texto completoBarteneva, Natasha S., Aigul Kussanova, Veronika Dashkova, Ayagoz Meirkhanova y Ivan A. Vorobjev. "Using Virtual Filtering Approach to Discriminate Microalgae by Spectral Flow Cytometer". En Methods in Molecular Biology, 23–40. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3020-4_2.
Texto completoSaylani, Hicham, Shahram Hosseini y Yannick Deville. "Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation". En Independent Component Analysis and Signal Separation, 322–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00599-2_41.
Texto completoMei, Tiemin, Jiangtao Xi, Fuliang Yin y Joe F. Chicharo. "Joint Diagonalization of Power Spectral Density Matrices for Blind Source Separation of Convolutive Mixtures". En Advances in Neural Networks – ISNN 2005, 520–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_85.
Texto completoAlekhin, A. D., S. G. Ostapchenko, D. B. Svydka y D. I. Malyarenko. "Spectral Kinetic and Correlation Characteristics of Inhomogeneous Mixtures in the Vicinity of the Critical Point of Stratification". En Light Scattering and Photon Correlation Spectroscopy, 441–60. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5586-1_37.
Texto completoChang, Chein-I. "Linear Spectral Mixture Analysis". En Real-Time Progressive Hyperspectral Image Processing, 37–73. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4419-6187-7_2.
Texto completoHannah, Robert W. "Infrared Spectra of Mixtures". En Course Notes on the Interpretation of Infrared and Raman Spectra, 461–504. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471690082.ch14.
Texto completoShimabukuro, Yosio Edemir y Flávio Jorge Ponzoni. "The Linear Spectral Mixture Model". En Springer Remote Sensing/Photogrammetry, 23–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02017-0_4.
Texto completoHutchins, Tiffany, Giacomo Vivanti, Natasa Mateljevic, Roger J. Jou, Frederick Shic, Lauren Cornew, Timothy P. L. Roberts et al. "Mixture Modeling". En Encyclopedia of Autism Spectrum Disorders, 1887. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_100887.
Texto completoActas de conferencias sobre el tema "Spectral mixtures"
Verzhbitskiy, I. A., M. Chrysos, A. P. Kouzov, F. Rachet, John Lewis y Adriana Predoi-Cross. "Double Raman Scattering In Gas Mixtures". En 20TH INTERNATIONAL CONFERENCE ON SPECTRAL LINE SHAPES. AIP, 2010. http://dx.doi.org/10.1063/1.3517542.
Texto completoMakarewicz, Joseph S. y Heather D. Makarewicz. "Spectral mixture decomposition using principal component analysis applied to pyroxene mixtures". En 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080604.
Texto completoBorysow, Aleksandra, Lothar Frommhold y Wilfried Meyer. "The collision induced rotovibrational absorption bands of hydrogen and hydrogen-helium mixtures-new results". En Spectral line shapes. AIP, 1990. http://dx.doi.org/10.1063/1.39899.
Texto completoWeiss, Shmuel. "Simulation of the collision-induced absorption spectrum of gaseous rare gas mixtures including ternary contributions". En Spectral line shapes. AIP, 1990. http://dx.doi.org/10.1063/1.39970.
Texto completoSolovjov, Vladimir P., Denis Lemonnier y Brent W. Webb. "SLW-1 Modeling of Radiative Heat Transfer in Non-Isothermal Non-Homogeneous Gas Mixtures With Soot". En 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22299.
Texto completoSubakan, Yusuf Cem, O. Celiktutan, A. T. Cemgil y B. Sankur. "Spectral learning of mixtures of Hidden Markov Models". En 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531340.
Texto completoHonciuc, Maria, Eugenia G. Carbunescu, Carmen Popa, Elena Slavnicu y Iulian Badragan. "Spectral study of some fatty acid-cholesterol mixtures". En SIOEL: Sixth Symposium of Optoelectronics, editado por Teodor Necsoiu, Maria Robu y Dan C. Dumitras. SPIE, 2000. http://dx.doi.org/10.1117/12.378644.
Texto completoField, Paul E. y Roger J. Combs. "Infrared spectral evaluation of methanol/ammonia vapor mixtures". En Optics East, editado por Arthur J. Sedlacek III, Steven D. Christesen, Tuan Vo-Dinh y Roger J. Combs. SPIE, 2004. http://dx.doi.org/10.1117/12.565164.
Texto completoKryukov, N. A., P. A. Saveliev y M. A. Tchaplyguine. "Radiation spectroscopy of heavy rare-gas excimers and their mixtures". En The 13th international conference on spectral line shapes. AIP, 1997. http://dx.doi.org/10.1063/1.51853.
Texto completoMehrubeoglu, M., P. V. Zimba, L. L. McLauchlan y M. Y. Teng. "Spectral unmixing of three-algae mixtures using hyperspectral images". En 2013 IEEE Sensors Applications Symposium (SAS). IEEE, 2013. http://dx.doi.org/10.1109/sas.2013.6493565.
Texto completoInformes sobre el tema "Spectral mixtures"
Pokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson y Shea Hammond. A novel laboratory method for the detection and identification of cyanobacteria using hyperspectral imaging : hyperspectral imaging for cyanobacteria detection. Engineer Research and Development Center (U.S.), junio de 2021. http://dx.doi.org/10.21079/11681/40966.
Texto completoIrizarry, Alfredo V. The Spectral Mixture Models: A Minimum Information Divergence Approach. Fort Belvoir, VA: Defense Technical Information Center, abril de 2010. http://dx.doi.org/10.21236/ada519885.
Texto completoDwyer, Roger F. Fourth-Order Spectra of Mixture and Modulated Processes. Fort Belvoir, VA: Defense Technical Information Center, octubre de 1988. http://dx.doi.org/10.21236/ada203398.
Texto completoSchlack, Trevor, Samuel Beal, Elizabeth Corriveau y Jay Clausen. Detection limits of trinitrotoluene and ammonium nitrate in soil by Raman spectroscopy. Engineer Research and Development Center (U.S.), febrero de 2022. http://dx.doi.org/10.21079/11681/43302.
Texto completoWoods, K. N. y H. Wiedemann. The Influence of Chain Dynamics on the Far Infrared Spectrum of Liquid Methanol-Water Mixtures. Office of Scientific and Technical Information (OSTI), julio de 2005. http://dx.doi.org/10.2172/878842.
Texto completoDawn, William C. An Analytic Benchmark for the Solution to the Isotopic Fission Spectrum Mixture Problem. Office of Scientific and Technical Information (OSTI), enero de 2020. http://dx.doi.org/10.2172/1593873.
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 completoAsenath-Smith, Emily, Emma Ambrogi, Eftihia Barnes y Jonathon Brame. CuO enhances the photocatalytic activity of Fe₂O₃ through synergistic reactive oxygen species interactions. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/42131.
Texto completoPaesani, Francesco y Wei Xiong. Probing the Structure and Dynamics of Fluid Mixtures in Porous Materials Through Ultrafast Vibrational Spectro-Microscopy and Many-Body Molecular Dynamics. Office of Scientific and Technical Information (OSTI), diciembre de 2022. http://dx.doi.org/10.2172/1901582.
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