Relevant bibliographies by topics / Spectral mixtures

Academic literature on the topic 'Spectral mixtures'

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Published: 6 September 2023

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Journal articles on the topic "Spectral mixtures"

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Lo, Su-Chin, and Chris W. Brown. "Infrared Spectral Search for Mixtures in Medium-Size Libraries." Applied Spectroscopy 45, no. 10 (December 1991): 1621–27. http://dx.doi.org/10.1366/0003702914335256.

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A new algorithm is presented for searching medium-size infrared spectral libraries for the components in spectra of mixtures. The algorithm treats the spectra in the library as an m-component quantitative analysis problem in which each of the library spectra represents a standard mixture having a concentration of 1.0 for that component. Principal component regression (PCR) is used to reduce the dimensionality of the problem and to provide the regression coefficients for determining pseudo-concentrations or composition indices (CI) in mixtures. The PCR analysis is followed by the application of an adaptive filter to remove all similarity of the first target component from the mixture and from a selected subgroup of the library. This is followed by a second PCR analysis on the modified spectral data to identify the next target compound. If the correct target components are selected with successive applications of the adaptive filter, the residuals will approach zero. All components in five two- and three-component mixtures were correctly identified by this new Mix-Match algorithm, whereas only two of the five mixtures were completely identified by a typical dot-product search routine.
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Ducasse, Etienne, Karine Adeline, Xavier Briottet, Audrey Hohmann, Anne Bourguignon, and 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, no. 11 (May 27, 2020): 1723. http://dx.doi.org/10.3390/rs12111723.

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Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils.
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Brown, Chris W., Anne E. Okafor, Steven M. Donahue, and Su-Chin Lo. "UV-Visible Spectral Library Search with Mixtures." Applied Spectroscopy 49, no. 7 (July 1995): 1022–27. http://dx.doi.org/10.1366/0003702953964723.

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A library mixture search method originally developed for infrared spectra has been successfully applied to UV-visible spectra. This novel approach for searching a spectral library performs a principal component analysis (PCA) on the entire library of spectra for pure compounds. The library spectra are represented by their PCA scores, and the concentrations (assumed to be unity) are regressed onto these scores. The scores for an unknown spectrum projected onto the PCA basis set are multiplied by the regression matrix to predict pseudo-concentrations or composition indices. After the first pass through the library, a subgroup of the top 20 hits (10% of the library) is selected and the PCR analysis is repeated on this set to improve the selection process. Spectra of each of the individual target components are adaptively filtered from the subgroup of library spectra and from the unknown spectrum prior to the repeat of the PCR analysis. The application of the adaptive filter greatly improves the success rate on hitting the second and third components by removing the first hit during each pass through the library. Computation times for training and applying the Mix-Match algorithm are greatly reduced by pre-processing with Fourier Transforms. A 200-compound library could be trained in 45 min and searched in 9 s; a 20-compound subgroup could be adaptively filtered and searched in 37 s. Both components in 12 two-component mixtures and one component in each of two two-component mixtures were correctly identified; the algorithm failed on both components in only one out of 15 two-component mixtures. All three components were correctly identified in one three-component mixture, and one component was correctly identified in another three-component mixture.
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Magazù, S., E. Calabrò, and M. T. Caccamo. "Experimental Study of Thermal Restraint in Bio-Protectant Disaccharides by FTIR Spectroscopy." Open Biotechnology Journal 12, no. 1 (July 31, 2018): 123–33. http://dx.doi.org/10.2174/1874070701812010123.

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Background: In the present paper, InfraRed (IR) spectra on water mixtures of two homologous disaccharides, i.e. sucrose and trehalose, as a function of temperature have been collected. Methods: In particular, IR spectra were registered, in the spectral range from 4000 cm-1 to 400 cm-1, to investigate the thermal response of the water mixtures of two homologous disaccharides, through positive thermal scans, i.e. by increasing the temperature from the value of 25°C to the value of 50°C. The OH-stretching region has been analyzed by means of two simple and straightforward procedures, i.e. by evaluating the shift of the intramolecular OH stretching center frequency and the Spectral Distance (SD). Result and Conclusion: Both the analyses indicate that trehalose water mixture have a higher thermal response than that of the sucrose-water mixture.
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Anastasiadis, Johannes, and Michael Heizmann. "GAN-regularized augmentation strategy for spectral datasets." tm - Technisches Messen 89, no. 4 (February 5, 2022): 278–88. http://dx.doi.org/10.1515/teme-2021-0109.

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Abstract Artificial neural networks are used in various fields including spectral unmixing, which is used to determine the proportions of substances involved in a mixture, and achieve promising results. This is especially true if there is a non-linear relationship between the spectra of mixtures and the spectra of the substances involved (pure spectra). To achieve sufficient results, neural networks need lots of representative training data. We present a method that extends existing training data for spectral unmixing consisting of spectra of mixtures by learning the mixing characteristic using an artificial neural network. Spectral variability is considered by random inputs. The network structure used is a generative adversarial net that takes the dependence on the abundances of pure substances into account by an additional term in its objective function, which is minimized during training. After training further data for abundance vectors for which there is no real measurement data in the original training dataset can be generated. A neural network trained with the augmented training dataset shows better performance in spectral unmixing compared to being trained with the original dataset. The presented network structure improves already existing results obtained with a generative convolutional neural network, which is superior to model-based approaches.
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Golyak, I. S., E. R. Kareva, I. L. Fufurin, D. R. Anfimov, A. V. Scherbakova, A. O. Nebritova, P. P. Demkin, and A. N. Morozov. "Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath." Computer Optics 46, no. 4 (August 2022): 650–58. http://dx.doi.org/10.18287/2412-6179-co-1058.

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In this paper, the application of machine learning and deep learning in the spectral analysis of multicomponent gas mixtures is considered. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3–12.8 µm, a peak power of up to 150 mW, and an astigmatic Herriott gas cell with an optical path length of up to 76 m. Acetone, ethanol, methanol, and their mixtures are used as test substances. For the detection and clustering of substances, including molecular biomarkers, methods of machine learning, such as stochastic embedding of neighbors with a t-distribution, principal component analysis and classification methods, such as random forest, gradient boosting, and logistic regression, are proposed. A shallow convolutional neural network based on TensorFlow (Google) and Keras is used for the spectral analysis of gas mixtures. Model spectra of substances are used as a training sample, and model and experimental spectra are used as a test sample. It is shown that neural networks trained on model spectra (NIST database) can recognize substances in experimental gas mixtures. We propose using machine learning methods for clustering and classification of pure substances and gas mixtures and neural networks for the identification of gas mixture components. Using the experimental setup described, the experimentally obtained concentration limits are 80 ppb for acetone and 100–120 ppb for ethanol and methanol. The possibility of using the proposed methods for analyzing spectra of human exhaled air is shown, which is significant for biomedical applications.
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Lo, Su-Chin, and Chris W. Brown. "Infrared Spectral Search for Mixtures in Large-Size Libraries." Applied Spectroscopy 45, no. 10 (December 1991): 1628–32. http://dx.doi.org/10.1366/0003702914335111.

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A routine for searching large spectral libraries with spectra of mixtures is presented. The dimensionality of a 3169-compound library is reduced to 12% of its original size by using Fourier transform compression and principal component analysis. A principal component regression is performed and used as a prefilter in selecting spectra having features (and chemical groups) similar to those of the unknown mixture. A dot-product metric is then used to identify a target component from the subgroup formed by the prefilter. This is followed by the application of an adaptive filter to remove the similarity of the target component from the subgroup and from the unknown mixture; the search is repeated on the modified data. Successive applications of the adaptive filter will produce minimum residuals if the correct identifications are made. Once the residuals are minimized, a similarity index is calculated to determine the closeness of the unknown mixture spectrum to a spectrum reconstructed from the library spectra. Four out of five two- and three-component spectra were correctly identified. One of the two components in the fifth mixture was correctly identified, and the residual values flagged the improper identification of the second component. After the adaptive filter was applied to the entire library, the second component was correctly identified. Results for this new algorithm are compared to those from four more traditional search routines, which were only completely successful on one of the unknown mixtures.
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Nyden, Marc R., and Krishnan Chittur. "Component Spectrum Reconstruction from Partially Characterized Mixtures." Applied Spectroscopy 43, no. 1 (January 1989): 123–28. http://dx.doi.org/10.1366/0003702894201743.

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A mathematical analysis of some existing approaches to component spectrum reconstruction is presented. This analysis leads to the derivation of a generalization of the cross-correlation technique. The effectiveness of these methods is assessed from the quality of the reconstructions obtained with the use of synthetic mixture spectra. Reconstructions of the spectra of the components of aqueous mixtures of immunoglobulin G and albumin are compared to the corresponding spectral reconstructions of the pure proteins in buffer.
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Ciarniello, 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, no. 11 (November 3, 2021): 1222. http://dx.doi.org/10.3390/min11111222.

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Visual-to-infrared (VIS-IR) remote sensing observations of different classes of outer solar system objects indicate the presence of water ice and organics. Here, we present laboratory reflectance spectra in the 0.5–4.2 μm spectral range of binary particulate mixtures of water ice, organics analogue (kerite), and an opaque iron sulphide phase (pyrrhotite) to investigate the spectral effects of varying mixing ratios, endmember grain size, and mixing modality. The laboratory spectra are also compared to different implementations of the Hapke reflectance model (Hapke, 2012). We find that minor amounts (≲1 wt%) of kerite (investigated grain sizes of 45–63 μm and <25 μm) can remain undetected when mixed in coarse-grained (67 ± 31 μm) water ice, suggesting that organics similar to meteoritic insoluble organic matter (IOM) might be characterized by larger detectability thresholds. Additionally, our measurements indicate that the VIS absolute reflectance of water ice-containing mixtures is not necessarily monotonically linked to water ice abundance. The latter is better constrained by spectral indicators such as the band depths of water ice VIS-IR diagnostic absorptions and spectral slopes. Simulation of laboratory spectra of intimate mixtures with a semi-empirical formulation of the Hapke model suggests that simplistic assumptions on the endmember grain size distribution and shape may lead to estimated mixing ratios considerably offset from the nominal values. Finally, laboratory spectra of water ice grains with fine-grained pyrrhotite inclusions (intraparticle mixture) have been positively compared with a modified version of the Hapke model from Lucey and Riner (2011).
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Kulko, Roman-David, Alexander Pletl, Andreas Hanus, and Benedikt Elser. "Detection of Plastic Granules and Their Mixtures." Sensors 23, no. 7 (March 24, 2023): 3441. http://dx.doi.org/10.3390/s23073441.

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Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.
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Dissertations / Theses on the topic "Spectral mixtures"

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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.

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Vlack, 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.

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Luo, 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.

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Raksuntorn, 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.

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Levi, 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.

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Un code de calcul raie par raie des propriétés radiatives de mélanges gazeux hétérogènes et anisothermes CO2, H2O, CO, N2, o a été construit a partir de données spectroscopiques, applicable de 300 à 2500 K de 0,1 à 50 ATM, et dans tout le domaine infrarouge. Réalisation d'un montage de mesure de la transitivité d'un gaz a température élevée
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Parra, Vásquez Gabriel Enrique. "Spectral mixture kernels for Multi-Output Gaussian processes." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/150553.

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Magíster en Ciencias de la Ingeniería, Mención Matemáticas Aplicadas. Ingeniero Civil Matemático
Multi-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.
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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.

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Raman, Pujita. "Speaker Identification and Verification Using Line Spectral Frequencies." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52964.

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State-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many feature compensation, model compensation and signal enhancement techniques have been proposed to improve the noise-robustness of SIV systems. Most of these techniques require extensive training, are computationally expensive or make assumptions about the noise characteristics. There has not been much focus on analyzing the relative importance, or speaker-discriminative power of different speech zones, particularly under noisy conditions. In this work, an automatic, text-independent speaker identification (SI) system and speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The performance of the proposed SI and SV systems are evaluated under various types of background noise. A score-level fusion based technique is implemented to extract complementary information from static and dynamic LSF features. The proposed score-level fusion based SI and SV systems are found to be more robust under noisy conditions. In addition, we investigate the speaker-discriminative power of different speech zones such as vowels, non-vowels and transitions. Rapidly varying regions of speech such as consonant-vowel transitions are found to be most speaker-discriminative in high SNR conditions. Steady, high-energy vowel regions are robust against noise and are hence most speaker-discriminative in low SNR conditions. We show that selectively utilizing features from a combination of transition and steady vowel zones further improves the performance of the score-level fusion based SI and SV systems under noisy conditions.
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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.

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Kressler, 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.

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Obtaining up-to-date information concernmg the environment at reasonable costs is a challenge faced by many institutions today. Satellite images meet both demands and thus present a very attractive source of information. The following thesis deals with the comparison of satellite images and a vector based land use data base of the City of Vienna. The satellite data is transformed using the spectral mixture analysis, which allows an investigation at a sub-pixel level. The results of the transformation are used to determine how suitable this spectral mixture analysis is to distinguish different land use classes in an urban area. In a next step the results of the spectral mixture analysis of two different images (recorded in 1986 and 1991) are used to undertake a change detection. The aim is to show those areas, where building activities have taken place. This information may aid the update of data bases, by limiting a detailed examination of an area to those areas, which show up as changes in the change detection. The proposed method is a fast and inexpensive way of analysing large areas and highlighting those areas where changes have taken place. lt is not limited to urban areas but may easily be adapted for different environments. (author's abstract)
Series: Research Reports of the Institute for Economic Geography and GIScience
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Books on the topic "Spectral mixtures"

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Hawaii Institute of Geophysics. Planetary Geosciences Division. and 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.

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Hawaii Institute of Geophysics. Planetary Geosciences Division. and 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.

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Shimabukuro, Yosio Edemir, and Flávio Jorge Ponzoni. Spectral Mixture for Remote Sensing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02017-0.

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Lin, Li, He Guoqi, and 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.

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Wehrmeyer, Joseph A. Temperature and mixture fraction profiles in counterflow diffusion flames using linewise Raman imaging. Washington, D. C: AIAA, 1995.

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So odd a mixture: Along the autistic spectrum in Pride and prejudice. London: Jessica Kingsley Publishers, 2007.

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United 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.

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Ruckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging. Elsevier Science & Technology Books, 2016.

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Ruckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 2016.

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Resolving 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.

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Book chapters on the topic "Spectral mixtures"

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Achlioptas, Dimitris, and Frank McSherry. "On Spectral Learning of Mixtures of Distributions." In Learning Theory, 458–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_31.

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Luo, Bin, and Sibao Chen. "LPP and LPP Mixtures for Graph Spectral Clustering." In Advances in Image and Video Technology, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_12.

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Barteneva, Natasha S., Aigul Kussanova, Veronika Dashkova, Ayagoz Meirkhanova, and Ivan A. Vorobjev. "Using Virtual Filtering Approach to Discriminate Microalgae by Spectral Flow Cytometer." In Methods in Molecular Biology, 23–40. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3020-4_2.

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AbstractFluorescence methods are widely used for the study of marine and freshwater phytoplankton communities. However, the identification of different microalgae populations by the analysis of autofluorescence signals remains a challenge. Addressing the issue, we developed a novel approach using the flexibility of spectral flow cytometry analysis (SFC) and generating a matrix of virtual filters (VF) which allowed thorough examination of autofluorescence spectra. Using this matrix, different spectral emission regions of algae species were analyzed, and five major algal taxa were discriminated. These results were further applied for tracing particular microalgae taxa in the complex mixtures of laboratory and environmental algal populations. An integrated analysis of single algal events combined with unique spectral emission fingerprints and light scattering parameters of microalgae can be used to differentiate major microalgal taxa. We propose a protocol for the quantitative assessment of heterogenous phytoplankton communities at the single-cell level and monitoring of phytoplankton bloom detection using a virtual filtering approach on a spectral flow cytometer (SFC-VF).
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Saylani, Hicham, Shahram Hosseini, and Yannick Deville. "Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation." In 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.

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Mei, Tiemin, Jiangtao Xi, Fuliang Yin, and Joe F. Chicharo. "Joint Diagonalization of Power Spectral Density Matrices for Blind Source Separation of Convolutive Mixtures." In Advances in Neural Networks – ISNN 2005, 520–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_85.

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Alekhin, A. D., S. G. Ostapchenko, D. B. Svydka, and D. I. Malyarenko. "Spectral Kinetic and Correlation Characteristics of Inhomogeneous Mixtures in the Vicinity of the Critical Point of Stratification." In Light Scattering and Photon Correlation Spectroscopy, 441–60. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5586-1_37.

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Chang, Chein-I. "Linear Spectral Mixture Analysis." In 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.

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Hannah, Robert W. "Infrared Spectra of Mixtures." In 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.

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Shimabukuro, Yosio Edemir, and Flávio Jorge Ponzoni. "The Linear Spectral Mixture Model." In Springer Remote Sensing/Photogrammetry, 23–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02017-0_4.

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Hutchins, Tiffany, Giacomo Vivanti, Natasa Mateljevic, Roger J. Jou, Frederick Shic, Lauren Cornew, Timothy P. L. Roberts, et al. "Mixture Modeling." In 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.

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Conference papers on the topic "Spectral mixtures"

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Verzhbitskiy, I. A., M. Chrysos, A. P. Kouzov, F. Rachet, John Lewis, and Adriana Predoi-Cross. "Double Raman Scattering In Gas Mixtures." In 20TH INTERNATIONAL CONFERENCE ON SPECTRAL LINE SHAPES. AIP, 2010. http://dx.doi.org/10.1063/1.3517542.

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Makarewicz, Joseph S., and Heather D. Makarewicz. "Spectral mixture decomposition using principal component analysis applied to pyroxene mixtures." In 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.

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Borysow, Aleksandra, Lothar Frommhold, and Wilfried Meyer. "The collision induced rotovibrational absorption bands of hydrogen and hydrogen-helium mixtures-new results." In Spectral line shapes. AIP, 1990. http://dx.doi.org/10.1063/1.39899.

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Weiss, Shmuel. "Simulation of the collision-induced absorption spectrum of gaseous rare gas mixtures including ternary contributions." In Spectral line shapes. AIP, 1990. http://dx.doi.org/10.1063/1.39970.

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Solovjov, Vladimir P., Denis Lemonnier, and Brent W. Webb. "SLW-1 Modeling of Radiative Heat Transfer in Non-Isothermal Non-Homogeneous Gas Mixtures With Soot." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22299.

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The Spectral Line Weighted-sum-of-gray-gases (SLW) model consisting only of a single gray gas and of one clear gas is developed as an efficient spectral method for modeling radiation transfer in gaseous medium. The model is applied here in prediction of radiative transfer in non-isothermal and non-homogeneous gas mixtures with non-gray soot. The absorption spectrum of the gas mixture and soot particles is treated as a spectrum of a single effective gas reducing the problem to the simplest case of the SLW model with a single gray gas. Good accuracy can be achieved by the optimal choice of the model’s gray gas absorption coefficient and its weight by application of the Absorption-Line Blackbody Distribution Functions of individual species in the mixture calculated with a high-resolution spectral database. The SLW-1 model is validated by comparison with benchmark solutions using the Line-by-Line method, the SLW method with a large number of gray gases, and the SNB model.
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Subakan, Yusuf Cem, O. Celiktutan, A. T. Cemgil, and B. Sankur. "Spectral learning of mixtures of Hidden Markov Models." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531340.

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Honciuc, Maria, Eugenia G. Carbunescu, Carmen Popa, Elena Slavnicu, and Iulian Badragan. "Spectral study of some fatty acid-cholesterol mixtures." In SIOEL: Sixth Symposium of Optoelectronics, edited by Teodor Necsoiu, Maria Robu, and Dan C. Dumitras. SPIE, 2000. http://dx.doi.org/10.1117/12.378644.

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Field, Paul E., and Roger J. Combs. "Infrared spectral evaluation of methanol/ammonia vapor mixtures." In Optics East, edited by Arthur J. Sedlacek III, Steven D. Christesen, Tuan Vo-Dinh, and Roger J. Combs. SPIE, 2004. http://dx.doi.org/10.1117/12.565164.

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Kryukov, N. A., P. A. Saveliev, and M. A. Tchaplyguine. "Radiation spectroscopy of heavy rare-gas excimers and their mixtures." In The 13th international conference on spectral line shapes. AIP, 1997. http://dx.doi.org/10.1063/1.51853.

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Mehrubeoglu, M., P. V. Zimba, L. L. McLauchlan, and M. Y. Teng. "Spectral unmixing of three-algae mixtures using hyperspectral images." In 2013 IEEE Sensors Applications Symposium (SAS). IEEE, 2013. http://dx.doi.org/10.1109/sas.2013.6493565.

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Reports on the topic "Spectral mixtures"

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Pokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson, and 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.), June 2021. http://dx.doi.org/10.21079/11681/40966.

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To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purposes. Hyperspectral signatures of different cyanobacteria were compared to evaluate spectral deviations between genera to assess the feasibility of using this imaging method in the field. Algorithms were then developed to spectrally deconvolute mixtures of cyanobacteria to determine relative abundances of each species. Last, laboratory cultures of Microcystis aeruginosa and Anabaena sp. were subjected to varying macro (nitrate and phosphate) and micro-nutrient (iron and magnesium) stressors to establish the stability of signatures within each species. Based on the findings, hyperspectral imaging can be a valuable tool for the detection and monitoring of cyanobacteria. However, it should be used with caution and only during stages of active growth for accurate identification and limited interference owing to stress.
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Irizarry, Alfredo V. The Spectral Mixture Models: A Minimum Information Divergence Approach. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada519885.

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Dwyer, Roger F. Fourth-Order Spectra of Mixture and Modulated Processes. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada203398.

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Schlack, Trevor, Samuel Beal, Elizabeth Corriveau, and Jay Clausen. Detection limits of trinitrotoluene and ammonium nitrate in soil by Raman spectroscopy. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43302.

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The detection limit of 2,4,6-trinitrotoluene (TNT) and ammonium nitrate (AN) in mixtures of Ottawa sand (OS) was studied using a Raman microscope applying conventional calibration curves, Pearson correlation coefficients, and two-sample t-tests. By constructing calibration curves, the conventionally defined detection limits were estimated to be 1.9 ± 0.4% by mass in OS and 1.9 ± 0.3% by mass in OS for TNT and AN. Both TNT and AN were detectable in concentrations as low as 1% by mass when Pearson correlation coefficients were used to compare averaged spectra to a library containing spectra from a range of soil types. AN was detectable in concentrations as low as 1% by mass when a test sample of spectra was compared to the same library using two-sample t-tests. TNT was not detectable at a concentration of 1% by mass when using two-sample t-tests.
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Woods, K. N., and H. Wiedemann. The Influence of Chain Dynamics on the Far Infrared Spectrum of Liquid Methanol-Water Mixtures. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/878842.

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Dawn, William C. An Analytic Benchmark for the Solution to the Isotopic Fission Spectrum Mixture Problem. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1593873.

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Griem, 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), October 1988. http://dx.doi.org/10.2172/7264387.

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Asenath-Smith, Emily, Emma Ambrogi, Eftihia Barnes, and Jonathon Brame. CuO enhances the photocatalytic activity of Fe₂O₃ through synergistic reactive oxygen species interactions. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42131.

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Iron oxide (α-Fe₂O₃, hematite) colloids were synthesized under hydrothermal conditions and investigated as catalysts for the photodegradation of an organic dye under broad-spectrum illumination. To enhance photocatalytic performance, Fe₂O₃ was combined with other transition-metal oxide (TMO) colloids (e.g., CuO and ZnO), which are sensitive to different regions of the solar spectrum (far visible and ultraviolet, respectively), using a ternary blending approach for compositional mixtures. For a variety of ZnO/Fe₂O₃/CuO mole ratios, the pseudo-first-order rate constant for methyl orange degradation was at least double the sum of the individual Fe₂O₃ and CuO rate constants, indicating there is an underlying synergy governing the photocatalysis reaction with these combinations of TMOs. A full compositional study was carried out to map the interactions between the three TMOs. Additional experiments probed the identity and role of reactive oxygen species and elucidated the mechanism by which CuO enhanced Fe₂O₃ photodegradation while ZnO did not. The increased photocatalytic performance of Fe2O3 in the presence of CuO was associated with hydroxyl radical ROS, consistent with heterogeneous photo-Fenton mechanisms, which are not accessible by ZnO. These results imply that low-cost photocatalytic materials can be engineered for high performance under solar illumination by selective pairing of TMOs with compatible ROS.
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Paesani, Francesco, and 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), December 2022. http://dx.doi.org/10.2172/1901582.

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