Journal articles on the topic 'Spectral mapping'

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1

Müller, Vladimir, and Aljoša Peperko. "Lower spectral radius and spectral mapping theorem for suprema preserving mappings." Discrete & Continuous Dynamical Systems - A 38, no. 8 (2018): 4117–32. http://dx.doi.org/10.3934/dcds.2018179.

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2

González, M., and V. M. Onieva. "On the spectral mapping theorem for essential spectra." Publicacions Matemàtiques 29 (November 1, 1985): 105–10. http://dx.doi.org/10.5565/publmat_292385_05.

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3

Verma, R. U., and S. S. Jou. "Some spectral inclusions onD-commuting systems." International Journal of Mathematics and Mathematical Sciences 13, no. 1 (1990): 115–20. http://dx.doi.org/10.1155/s016117129000014x.

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4

Pe’eri, Oren, Michael A. Golub, and Menachem Nathan. "Mapping of spectral signatures with snapshot spectral imaging." Applied Optics 56, no. 15 (May 12, 2017): 4309. http://dx.doi.org/10.1364/ao.56.004309.

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5

Aiena, Pietro, and Maria Teresa Biondi. "Some spectral mapping theorems through local spectral theory." Rendiconti del Circolo Matematico di Palermo 53, no. 2 (June 2004): 165–84. http://dx.doi.org/10.1007/bf02872869.

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6

Brodský, L., A. Klement, V. Penížek, R. Kodešová, and L. Borůvka. "Building soil spectral library of the Czech soils for quantitative digital soil mapping." Soil and Water Research 6, No. 4 (November 28, 2011): 165–72. http://dx.doi.org/10.17221/24/2011-swr.

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  Spectral libraries are the data archives of spectral signatures measured on natural and/or man-made materials. Here, the objective is to build a soil spectral library of the Czech soils (SSL-CZ). Further on, the overall aim is to apply diffuse reflectance spectroscopy as a tool for digital soil mapping. An inevitable part of the library is a metadata database that stores the corresponding auxiliary information on the soils: type of material (soil, parent material), sample preparation, location of the sample with geographic coordinates, soil classification, morphological features, soil laboratory measurements – chemical, physical, and potential biological properties, geophysical features of and climatological information on the sample location. The metadata database consists of seven general tables (General, Spatial, Soil class, Environmental, Auxiliary, Analytical and Spectra) relationally linked together. The stored information allows for a wide range of analyses and for modelling developments of digital soil mapping applications. An example of partial least-square regression (PLSR) modelling for soil pH and clay content with 0.84 and 0.68 coefficients of determination is provided on the subset of the collected data. Currently, the SSL-CZ database contains more than 500 records in the first phase of development. Spectral reflectance signatures are stored in the range of 350 to 2500 nm with a step of 1 nm measured by ASD FieldSpec 3. The soil spectral library developed is fully compatible with Global Soil Spectral Library (Soil Spectroscopy Group).
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7

Mazur, T., and M. Skwarczyński. "Spectral properties of holomorphic automorphism with fixed point." Glasgow Mathematical Journal 28, no. 1 (January 1986): 25–30. http://dx.doi.org/10.1017/s0017089500006297.

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The Hilbert space methods in the theory of biholomorphic mappings were applied and developed by S. Bergman [1, 2]. In this approach the central role is played by the Hilbert space L2H(D) consisting of all functions which are square integrable and holomorphic in a domain D ⊂ ℂN. A biholomorphic mapping φ:D ⃗ G induces the unitary mapping Uφ:L2H(G) ⃗ L2H(D) defined by the formulaHere ∂φ/∂z denotes the complex Jacobian of φ. The mapping Uϕ is useful, since it permits to replace a problem for D by a problem for its biholomorphic image G (see for example [11], [13]). When ϕ is an automorphism of D we obtain a unitary operator Uϕ on L2H(D).
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8

Leahy, J. P., and T. W. B. Muxlow. "Spectral Mapping of Classical Double Radio Sources." Symposium - International Astronomical Union 199 (2002): 179–88. http://dx.doi.org/10.1017/s0074180900168834.

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We review the evidence that spectral curvature in the extended emission of radio galaxies is caused by synchrotron losses, and that the spatial variation can be interpreted to yield ages and expansion speeds. One of the biggest worries has been the true value of the magnetic field, but X-ray detections of inverse-Compton radiation are beginning to suggest that “minimum energy” estimates are remarkably accurate. A critical test is to compare model and observed spectra over a broad frequency range; to date this has has only been done for Cygnus A, and the results proved controversial. Here we discuss several more cases and begin to draw some general conclusions.Hotspots are usually well fitted by continuous injection models, as expected. In two cases the implied injection index is flatter than 0.5, too flat to be produced by standard Fermi acceleration in a non-relativistic shock. The bridge spectra are reasonably fitted by single-burst models, but in some objects the injection index is not constant across the lobes, showing instead a tendency to steepen in the inner bridge, where the break frequencies are lowest. The true spectral shape may be a more gradual curve than the standard models, possibly because of mixing of electron populations with different ages. Our results are limited by the inaccuracy of the absolute flux density scale, especially at low frequencies, and a fresh attack on the flux scale would be timely.
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9

Tsutsumi, Shohei, Mitchell R. Rosen, and Roy S. Berns. "Spectral Gamut Mapping using LabPQR." Journal of Imaging Science and Technology 51, no. 6 (2007): 473. http://dx.doi.org/10.2352/j.imagingsci.technol.(2007)51:6(473).

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10

HABA, Kazumoto, Wataru HOTTA, Akihito HATA, Kazuaki WATANABE, and Muneo HORI. "SPECTRAL STOCHASTIC RETURN MAPPING ALGORITHM." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 73, no. 1 (2017): 34–45. http://dx.doi.org/10.2208/jscejam.73.34.

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11

King, R. A., and A. Clifton. "Spectral reconfiguration, permutation and mapping." Electronics Letters 38, no. 2 (2002): 98. http://dx.doi.org/10.1049/el:20020045.

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12

de Kat, Roeland, and Bharathram Ganapathisubramani. "Frequency–wavenumber mapping in turbulent shear flows." Journal of Fluid Mechanics 783 (October 15, 2015): 166–90. http://dx.doi.org/10.1017/jfm.2015.558.

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Spatial turbulence spectra for high-Reynolds-number shear flows are usually obtained by mapping experimental frequency spectra into wavenumber space using Taylor’s hypothesis, but this is known to be less than ideal. In this paper, we propose a cross-spectral approach that allows us to determine the entire wavenumber–frequency spectrum using two-point measurements. The method uses cross-spectral phase differences between two points – equivalent to wave velocities – to reconstruct the wavenumber–frequency plane, which can then be integrated to obtain the spatial spectrum. We verify the technique on a particle image velocimetry (PIV) data set of a turbulent boundary layer. To show the potential influence of the different mappings, the transfer functions that we obtained from our PIV data are applied to hot-wire data at approximately the same Reynolds number. Comparison of the newly proposed technique with the classic approach based on Taylor’s hypothesis shows that – as expected – Taylor’s hypothesis holds for larger wavenumbers (small spatial scales), but there are significant differences for smaller wavenumbers (large spatial scales). In the range of Reynolds number examined in this study, double-peaked spectra in the outer region of a turbulent wall flow are thought to be the result of using Taylor’s hypothesis. This is consistent with previous studies that focused on examining the limitations of Taylor’s hypothesis (del Álamo & Jiménez, J. Fluid Mech., vol. 640, 2009, pp. 5–26). The newly proposed mapping method provides a data-driven approach to map frequency spectra into wavenumber spectra from two-point measurements and will allow the experimental exploration of spatial spectra in high-Reynolds-number turbulent shear flows.
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13

Han, Xiaolin, Jing Yu, Jiqiang Luo, and Weidong Sun. "Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks." Remote Sensing 11, no. 10 (May 16, 2019): 1173. http://dx.doi.org/10.3390/rs11101173.

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Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains.
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14

Badola, Anushree, Santosh K. Panda, Dar A. Roberts, Christine F. Waigl, Uma S. Bhatt, Christopher W. Smith, and Randi R. Jandt. "Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska." Remote Sensing 13, no. 9 (April 27, 2021): 1693. http://dx.doi.org/10.3390/rs13091693.

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Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.
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15

Connah, David, Mark S. Drew, and Graham D. Finlayson. "Spectral edge: gradient-preserving spectral mapping for image fusion." Journal of the Optical Society of America A 32, no. 12 (November 23, 2015): 2384. http://dx.doi.org/10.1364/josaa.32.002384.

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16

Vázquez-Martín, R., P. Núñez, and A. Bandera. "LESS-mapping: Online environment segmentation based on spectral mapping." Robotics and Autonomous Systems 60, no. 1 (January 2012): 41–54. http://dx.doi.org/10.1016/j.robot.2011.08.011.

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17

Dosiev, Anar. "Cartan–Slodkowski spectra, splitting elements and noncommutative spectral mapping theorems." Journal of Functional Analysis 230, no. 2 (January 2006): 446–93. http://dx.doi.org/10.1016/j.jfa.2005.03.014.

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18

Duan, Yanting, Chaodong Wu, Xiaodong Zheng, Yucheng Huang, and Jian Ma. "Coherence based on spectral variance analysis." GEOPHYSICS 83, no. 3 (May 1, 2018): O55—O66. http://dx.doi.org/10.1190/geo2017-0158.1.

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The eigenstructure-based coherence attribute is a type of efficient and mature tool for mapping geologic edges such as faults and/or channels in the 3D seismic interpretation. However, the eigenstructure-based coherence algorithm is sensitive to low signal-to-noise ratio seismic data, and the coherence results are affected by the dipping structures. Due to the large energy gap between the low- and high-frequency components, the low-frequency components play the principal role in coherence estimation. In contrast, the spectral variance balances the difference between the low- and high-frequency components at a fixed depth. The coherence estimation based on amplitude spectra avoids the effect of the time delays resulting from the dipping structures. Combining the spectral variance with the amplitude spectra avoids the effect of dipping structures and enhances the antinoise performance of the high-frequency components. First, we apply the short-time Fourier transform to obtain the time-frequency spectra of seismic data. Next, we compute the variance values of amplitude spectra. Then, we apply the fast Fourier transform to obtain the amplitude spectra of spectral variance. Finally, we calculate the eigenstructure coherence by using the amplitude spectra of spectral variance as the input. We apply the method to the theoretical models and practical seismic data. In the Marmousi velocity model, the coherence estimation using the amplitude spectra of the spectral variance as input shows more subtle discontinuities, especially in deeper layers. The results from field-data examples demonstrate that the proposed method is helpful for mapping faults and for improving the narrow channel edges’ resolution of interest. Therefore, the coherence algorithm based on the spectral variance analysis may be conducive to the seismic interpretation.
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19

Al-Dulaimi, Al-Waled, Todd K. Moon, and Jacob H. Gunther. "Voice Transformation Using Two-Level Dynamic Warping and Neural Networks." Signals 2, no. 3 (July 14, 2021): 456–74. http://dx.doi.org/10.3390/signals2030028.

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Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network. An outer warping process which temporally aligns blocks of speech (dynamic time warp, DTW) invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp, DFW). The mapping function produced by inner dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Artifacts arising from this amplitude spectral mapping are reduced by reconstructing phase information. Information obtained by this process is used to train an artificial neural network to produce spectral warping information based on spectral input data. The performance of the speech mapping compared using Mel-Cepstral Distortion (MCD) with previous voice transformation research, and it is shown to perform better than other methods, based on their reported MCD scores.
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20

Chafai, Ezzeddine, and Tereza Álvarez. "Ascent, essential ascent, descent and essential descent for a linear relation in a linear space." Filomat 31, no. 3 (2017): 709–21. http://dx.doi.org/10.2298/fil1703709c.

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For a linear relation in a linear space some spectra defined by means of ascent, essential ascent, descent and essential descent are introduced and studied. We prove that the algebraic ascent, essential ascent, descent and essential descent spectrum of a linear relation in a linear space satisfy the polynomial spectral mapping theorem. As an application of the obtained results we show that the topological ascent, essential ascent, descent and essential descent spectrum verify the polynomial spectral mapping theorem.
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21

Rutten, René G. M. "Eclipse mapping and related techniques." Symposium - International Astronomical Union 176 (1996): 69–84. http://dx.doi.org/10.1017/s007418090008311x.

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Eclipse mapping is a technique to deduce spatial structure on very small angular scales in eclipsing cataclysmic variable stars (CVs). By analysing the eclipse light curve, information is obtained on the brightness structure of the accretion disk and of the compact mass-accreting object in these systems. This information would otherwise be well beyond the resolving power of any optical telescope. Since the development of the eclipse mapping technique by K. Horne, about one decade ago, it has now become an important tool in the study of CVs. Originally eclipse mapping was employed to construct brightness maps of accretion disks in broad spectral bands. Recently, maps of much higher spectral resolution have become available from which optical and UV spectra have been reconstructed in spatial detail across accretion disks. Such information is very important for our understanding of the physics of the accretion process.In this paper I will describe the eclipse mapping technique and review recent results. In conjunction, I will briefly highlight other techniques related to the mapping of surface structure in CVs.
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22

Shul'man, V. "Invariant subspaces and spectral mapping theorems." Banach Center Publications 30, no. 1 (1994): 313–25. http://dx.doi.org/10.4064/-30-1-313-325.

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23

Samadzadegan, Sepideh, and Philipp Urban. "Spatio-Spectral Gamut Mapping and Separation." Journal of Imaging Science and Technology 59, no. 4 (July 1, 2015): 404021–4040212. http://dx.doi.org/10.2352/j.imagingsci.technol.2015.59.4.040402.

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24

Wu, Guangyuan. "Charamer mismatch-based spectral gamut mapping." Laser Physics Letters 16, no. 9 (August 9, 2019): 095206. http://dx.doi.org/10.1088/1612-202x/ab36b4.

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25

Smith, J. D. T., L. Armus, D. A. Dale, H. Roussel, K. Sheth, B. A. Buckalew, T. H. Jarrett, G. Helou, and R. C. Kennicutt, Jr. "Spectral Mapping Reconstruction of Extended Sources." Publications of the Astronomical Society of the Pacific 119, no. 860 (October 2007): 1133–44. http://dx.doi.org/10.1086/522634.

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26

Chō, M., M. Giga, Y. M. Han, and T. Huruya. "Spectral Mapping Theorems for Hyponormal Operators." Mathematical Proceedings of the Royal Irish Academy 103, no. 2 (January 1, 2003): 209–16. http://dx.doi.org/10.3318/pria.2003.103.2.209.

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27

BARCLAY, STEVEN. "CONTINUITY OF THE SPECTRAL FACTORIZATION MAPPING." Journal of the London Mathematical Society 70, no. 03 (December 2004): 763–79. http://dx.doi.org/10.1112/s0024610704005575.

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28

Urban, Philipp, and Roy S. Berns. "Paramer Mismatch-Based Spectral Gamut Mapping." IEEE Transactions on Image Processing 20, no. 6 (June 2011): 1599–610. http://dx.doi.org/10.1109/tip.2010.2095872.

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29

Day, Colin R. "Spectral mapping theorem for integrated semigroups." Semigroup Forum 47, no. 1 (December 1993): 359–72. http://dx.doi.org/10.1007/bf02573773.

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30

Philpot, William, Stéphane Jacquemoud, and Jia Tian. "ND-space: Normalized difference spectral mapping." Remote Sensing of Environment 264 (October 2021): 112622. http://dx.doi.org/10.1016/j.rse.2021.112622.

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31

Yuan, Jiangtao, and Caihong Wang. "Spectral mapping theorems for Weyl spectrum and isolated spectral points." Operators and Matrices, no. 2 (2019): 349–61. http://dx.doi.org/10.7153/oam-2019-13-25.

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32

van der Meer, Freek. "Spectral mixture modelling and spectral stratigraphy in carbonate lithofacies mapping." ISPRS Journal of Photogrammetry and Remote Sensing 51, no. 3 (June 1996): 150–62. http://dx.doi.org/10.1016/0924-2716(95)00009-7.

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33

Benjamin, Ronalda. "Spectral mapping theorems for the upper Weyl and upper Browder spectra." Quaestiones Mathematicae 41, no. 7 (February 8, 2018): 951–61. http://dx.doi.org/10.2989/16073606.2017.1417924.

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34

Aiena, Pietro. "On the spectral mapping theorem for Weyl spectra of Toeplitz operators." Advances in Operator Theory 5, no. 4 (May 15, 2020): 1618–34. http://dx.doi.org/10.1007/s43036-020-00069-3.

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35

Pancoska, Petr, Vit Janota, and Timothy A. Keiderling. "Interconvertibility of Electronic and Vibrational Circular Dichroism Spectra of Proteins: A Test of Principle Using Neural Network Mapping." Applied Spectroscopy 50, no. 5 (May 1996): 658–68. http://dx.doi.org/10.1366/0003702963905916.

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Electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) are compared with respect to their interconvertibility for protein structural studies. ECD and amide I' VCD spectra of 28 proteins were used with a backpropagation projection neural network with one hidden layer to develop a mapping between the two spectral types. After the network converged, the number of neurons in the hidden layer was optimized by principal component analysis of the synaptic weights of the pilot network topology with redundant hidden neurons. Actual prediction of one spectrum from the other for individual proteins was tested by retraining these networks with 28 reduced training sets having one protein systematically left out. Comparison of network-predicted spectra with experimental ones is used to identify those spectral features which are unique in each method. Similarly, the VCD spectra of 23 proteins measured in both D2O and H2O in the amide I region were mapped onto each other with the use of the same type of neural network calculation. The results show that the effects of partial deuteration on the VCD spectra band shape are predictable from the H2O spectra. An analysis of the synaptic weights of the optimized networks was performed which allowed identification of the linear and nonlinear parts of the obtained mappings. Insight into the details of how the neural networks encode and process the spectroscopic information is derived from a spectral representation of these weight matrices.
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Duan, Yi Jun, Rui Si Zhang, Min Zeng, and Jian Ping Chen. "ASTER Spectral Analysis and Lithologic Mapping in Bangong Suture, Tibet, China." Advanced Materials Research 664 (February 2013): 403–8. http://dx.doi.org/10.4028/www.scientific.net/amr.664.403.

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The study applies ASTER imagery for lithologic mapping in Nianqu, Gaize, Tibet, which is a part of the west segment of the Bang suture. According to the mineralogy and lithology of the study area, spectral characteristic of the rock-forming minerals and alteration minerals are analyzed. After resampling the characteristic spectra in to the ASTER spectral resolution, pertinent image processing methods are used to extract the diagnostic features of respective lithology. The results show that the methods and procedures described in this study is applicable to other relevant areas of the Bangong suture.
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Šubr, Martin, Martin Petr, Vlastimil Peksa, Ondřej Kylián, Jan Hanuš, and Marek Procházka. "Ag Nanorod Arrays for SERS: Aspects of Spectral Reproducibility, Surface Contamination, and Spectral Sensitivity." Journal of Nanomaterials 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/729231.

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Ag nanorod arrays prepared by oblique angle vapor deposition (OAD) represent regular, large area substrates for surface-enhanced Raman scattering (SERS) spectroscopy. We studied uniformity and spectral reproducibility of silver OAD-fabricated substrates (AgOADs) by spectral mapping of methylene blue. The results demonstrate good reproducibility apart from occasional “hot-spot” sites where the intensity is higher. The number of “hot-spots” represents 2%–6% of SERS-active sites of mapping substrate area. We were able to obtain good SERS spectra of testing amino acid tryptophan at 1 × 10−5 M concentration and three different free-base porphyrins down to ∼10−7 M concentration. We found out that keeping the AgOADs in a vacuum chamber overnight prevents the surface from binding any contaminants from the ambient atmosphere, without significant reduction in the SERS enhancement. Such substrates provide stable SERS enhancement even when stored for 1 year after preparation.
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Sengar, V. K., A. S. Venkatesh, P. K. Champaty Ray, S. L. Chattoraj, and R. U. Sharma. "MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (October 14, 2016): 991–93. http://dx.doi.org/10.5194/isprs-archives-xli-b7-991-2016.

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The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4&amp;ndash;2.5&amp;thinsp;μm range has also been proved significant in the field of spaceborne mineral potential mapping. <br><br> Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.
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39

Järvinen, S. P., K. G. Strassmeier, T. A. Carroll, I. Ilyin, and M. Weber. "Mapping EK Draconis with PEPSI." Astronomy & Astrophysics 620 (December 2018): A162. http://dx.doi.org/10.1051/0004-6361/201833496.

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Aims. We present the first temperature surface map of EK Dra from very-high-resolution spectra obtained with the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI) at the Large Binocular Telescope. Methods. Changes in spectral line profiles are inverted to a stellar surface temperature map using our iMap code. The long-term photometric record is employed to compare our map with previously published maps. Results. Four cool spots were reconstructed, but no polar spot was seen. The temperature difference to the photosphere of the spots is between 990 and 280 K. Two spots are reconstructed with a typical solar morphology with an umbra and a penumbra. For the one isolated and relatively round spot (spot A), we determine an umbral temperature of 990 K and a penumbral temperature of 180 K below photospheric temperature. The umbra to photosphere intensity ratio of EK Dra is approximately only half of that of a comparison sunspot. A test inversion from degraded line profiles showed that the higher spectral resolution of PEPSI reconstructs the surface with a temperature difference that is on average 10% higher than before and with smaller surface areas by ~10–20%. PEPSI is therefore better suited to detecting and characterising temperature inhomogeneities. With ten more years of photometry, we also refine the spot cycle period of EK Dra to 8.9 ± 0.2 yr with a continuing long-term fading trend. Conclusions. The temperature morphology of spot A so far appears to show the best evidence for the existence of a solar-like penumbra for a starspot. We emphasise that it is more the non-capture of the true umbral contrast rather than the detection of the weak penumbra that is the limiting factor. The relatively small line broadening of EK Dra, together with the only moderately high spectral resolutions previously available, appear to be the main contributors to the lower-than-expected spot contrasts when comparing to the Sun.
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40

Nidamanuri, Rama Rao, and Bernd Zbell. "A method for selecting optimal spectral resolution and comparison metric for material mapping by spectral library search." Progress in Physical Geography: Earth and Environment 34, no. 1 (January 22, 2010): 47–58. http://dx.doi.org/10.1177/0309133309356376.

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Use of spectral library searching as an automated method of analysing hyperspectral remote sensing data for material mapping is gaining prominence, especially in the mineral mapping domain. The possibility and reliability of material identification by the spectral library search approach depends on the spectral representation, characterized by spectral resolution (or sampling interval) and comparison metric used. We present a method referred to as Relative Search Performance (RSP) for an evaluation of various spectral representations and comparison metrics for designing an optimal library search system for material mapping. The proposed method works on the basis of tracking the changes in the spectral matching ranks of entries in the hit lists of spectral library searches for various spectral representations and comparison metrics relative to a chosen standard. The method has been tested for the comparison of the search performance of various discrete spectral sampling intervals and popular comparison metrics using the USGS Spectral Library. Results indicate that this approach can be used for the selection of optimal spectral representation and/or for selecting a comparison metric appropriate for a particular material mapping application by the reflectance spectral library search.
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41

Checa-Garcia, R., J. Landgraf, A. Galli, F. Hase, V. A. Velazco, H. Tran, V. Boudon, F. Alkemade, and A. Butz. "Mapping spectroscopic uncertainties into prospective methane retrieval errors from Sentinel-5 and its precursor." Atmospheric Measurement Techniques 8, no. 9 (September 8, 2015): 3617–29. http://dx.doi.org/10.5194/amt-8-3617-2015.

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Abstract. Sentinel-5 (S5) and its precursor (S5P) are future European satellite missions aiming at global monitoring of methane (CH4) column-average dry air mole fractions (XCH4). The spectrometers to be deployed onboard the satellites record spectra of sunlight backscattered from the Earth's surface and atmosphere. In particular, they exploit CH4 absorption in the shortwave infrared spectral range around 1.65 μm (S5 only) and 2.35 μm (both S5 and S5P) wavelength. Given an accuracy goal of better than 2 % for XCH4 to be delivered on regional scales, assessment and reduction of potential sources of systematic error such as spectroscopic uncertainties is crucial. Here, we investigate how spectroscopic errors propagate into retrieval errors on the global scale. To this end, absorption spectra of a ground-based Fourier transform spectrometer (FTS) operating at very high spectral resolution serve as estimate for the quality of the spectroscopic parameters. Feeding the FTS fitting residuals as a perturbation into a global ensemble of simulated S5- and S5P-like spectra at relatively low spectral resolution, XCH4 retrieval errors exceed 0.6 % in large parts of the world and show systematic correlations on regional scales, calling for improved spectroscopic parameters.
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42

Checa-Garcia, R., J. Landgraf, F. Hase, H. Tran, V. Boudon, F. Alkemade, and A. Butz. "Mapping spectroscopic uncertainties into prospective methane retrieval errors from Sentinel-5 and its precursor." Atmospheric Measurement Techniques Discussions 8, no. 1 (January 29, 2015): 1333–63. http://dx.doi.org/10.5194/amtd-8-1333-2015.

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Abstract. Sentinel-5 (S5) and its precursor (S5P) are future European satellite missions aiming at global monitoring of methane (CH4) column average dry air mole fractions (XCH4). The spectrometers to be deployed on-board the satellites record spectra of sunlight backscattered from the Earth's surface and atmosphere. In particular, they exploit CH4 absorption in the shortwave infrared spectral range around 1.65 μm (S5 only) and 2.35 μm (both, S5 and S5P) wavelength. Given an accuracy goal of better than 2% for XCH4 to be delivered on regional scales, assessment and reduction of potential sources of systematic error such as spectroscopic uncertainties is crucial. Here, we investigate how spectroscopic errors propagate into retrieval errors on the global scale. To this end, absorption spectra of a ground-based Fourier Transform Spectrometer (FTS) operating at very high spectral resolution serve as estimate for the quality of the spectroscopic parameters. Feeding the FTS fitting residuals as a perturbation into a global ensemble of simulated S5 and S5P-like spectra at relatively low spectral resolution, XCH4 retrieval errors exceed 1% in large parts of the world and show systematic correlations on regional scales, calling for improved spectroscopic parameters.
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43

Poppiel, Raúl Roberto, Marilusa Pinto Coelho Lacerda, Rodnei Rizzo, José Lucas Safanelli, Benito Roberto Bonfatti, Nélida Elizabet Quiñonez Silvero, and José Alexandre Melo Demattê. "Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil." Remote Sensing 12, no. 7 (April 8, 2020): 1197. http://dx.doi.org/10.3390/rs12071197.

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Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.
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44

Ming, Yan Fang, and Li Yang. "The Methods of Mapping Lithology with AVIRIS Data." Advanced Materials Research 955-959 (June 2014): 3879–82. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3879.

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A hyper-spectral remote sensing instrument AVIRIS was used to map lithology. Ground measurement data of lithology from ASTER Spectral Library were used to analyze the characters of the spectrum and form the model to estimate the type of lithologies. we process the spectrum with the methods of spectral angle mapping, and spectral absorption index etc. To enhance the significance of the spectrum character, we used the methods of spectral angle mapping, and spectral absorption index etc to process the spectrum. An AVIRIS data covers the Cuprite mining district in western Nevada, USA was used to do the experiment, result shows that the lithologies got from the AVIRIS have a high consistence with groud measurement.
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45

Zhang, Han. "Mineral Information Extraction Using Hyper Spectral Remote Sensing Technology." Applied Mechanics and Materials 340 (July 2013): 480–83. http://dx.doi.org/10.4028/www.scientific.net/amm.340.480.

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Based on the hyper spectral data provided by small satellites for environmental hazard monitoring and forecasting (HJ-A), we convert the data format and reflectance to remove possible interference. Spectral angle mapping method is employed afterwards for mineral mapping of the study area. The small-scaled mineral distribution map is then generated for the whole study area. Our study suggests that mineral mapping using HJ-A satellite hyper spectral data is fairly effective in terms of mapping quality and cost.
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46

Koirala, Bikram, Mahdi Khodadadzadeh, Cecilia Contreras, Zohreh Zahiri, Richard Gloaguen, and Paul Scheunders. "A Supervised Method for Nonlinear Hyperspectral Unmixing." Remote Sensing 11, no. 20 (October 22, 2019): 2458. http://dx.doi.org/10.3390/rs11202458.

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Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning nonlinear mapping, based on Gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset shows that this novel methodology is very promising.
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47

Wawrzyńczyk, Antoni. "Schur Lemma and the Spectral Mapping Formula." Bulletin of the Polish Academy of Sciences Mathematics 55, no. 1 (2007): 63–69. http://dx.doi.org/10.4064/ba55-1-7.

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48

Hernández-Garciadiego, Carlos. "A note on the spectral mapping theorem." Studia Mathematica 83, no. 2 (1986): 201–4. http://dx.doi.org/10.4064/sm-83-2-201-204.

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49

Seferoğlu, H. "A spectral mapping theorem for Banach modules." Studia Mathematica 156, no. 2 (2003): 99–103. http://dx.doi.org/10.4064/sm156-2-1.

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50

Kikongi, Philippe, Hendrix Demers, Raynald Gauvin, Ryan Gosselin, and Nadi Braidy. "Mapping Data with Heavily Overlapped Spectral Features." Microscopy and Microanalysis 23, S1 (July 2017): 216–17. http://dx.doi.org/10.1017/s1431927617001763.

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