Relevant bibliographies by topics / Spectral analysis

Academic literature on the topic 'Spectral analysis'

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Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Spectral analysis"

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Guo, Hao, Kurt J. Marfurt, and Jianlei Liu. "Principal component spectral analysis." GEOPHYSICS 74, no. 4 (July 2009): P35—P43. http://dx.doi.org/10.1190/1.3119264.

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Spectral decomposition methods help illuminate lateral changes in porosity and thin-bed thickness. For broadband data, an interpreter might generate 80 or more somewhat redundant amplitude and phase spectral components spanning the usable seismic bandwidth at [Formula: see text] intervals. Large numbers of components can overload not only the interpreter but also the display hardware. We have used principal component analysis to reduce the multiplicity of spectral data and enhance the most energetic trends inside the data. Each principal component spectrum is mathematically orthogonal to other spectra, with the importance of each spectrum being proportional to the size of its corresponding eigenvalue. Principal components are ideally suited to identify geologic features that give rise to anomalous moderate- to high-amplitude spectra. Unlike the input spectral magnitude and phase components, the principal component spectra are not direct indicators of bed thickness. By combining the variability of multiple components, principal component spectra highlight stratigraphic features that can be interpreted using a seismic geomorphology workflow. By mapping the three largest principal components using the three primary colors of red, green, and blue, we could represent more than 80% of the spectral variance with a single image. We have applied and validated this workflow using a broadband data volume containing channels draining an unconformity, which was acquired over the Central Basin Platform, Texas, U.S.A. Principal component analysis reveals a channel system with only a few output data volumes. The same process provides the interpreter with flexibility to remove any unwanted high-amplitude geologic trends or random noise from the original spectral components by eliminating those principal components that do not aid in delineation of prospective features with their interpretation during the reconstruction process.
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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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Chen, Qiao, Li Jie Wang, and Stephen Westland. "Analysis of Hyperspectral Images Based on PCA." Advanced Materials Research 187 (February 2011): 641–46. http://dx.doi.org/10.4028/www.scientific.net/amr.187.641.

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Recent computational models of color vision demonstrate that it is possible to achieve exact color constancy over a limited range of lights and surfaces described by a low-dimensional linear model. For smooth reflectance spectra the different spectral bands have a significant degree of correlation. Any spectral reflectance distribution can be approximated to a specified degree of accuracy as a weighted sum of basis functions. Reflectance spectra of hyperspectral images of the natural scenes are supposed to represent the real world better than any certain classes of natural and man-made spectral reflectance data sets such as rocks, leaves, Munsell chips, etc. The characteristics of the spectra will be important to understand the spectral properties of the object reflectance and the representation of spectral images by linear models. In this study sets of hyperspectral images have been analyzed by principal-component analysis (PCA) method for spectral encoding.
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While, James, Andrew Jackson, Dirk Smit, and Ed Biegert. "Spectral analysis of gravity gradiometry profiles." GEOPHYSICS 71, no. 1 (January 2006): J11—J22. http://dx.doi.org/10.1190/1.2169848.

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The gravity gradient tensor (whose components are the second derivatives of the gravitational potential) is a symmetric tensor that, ignoring the constraint imposed by Laplace's equation, contains only six independent components. When measured on a horizontal plane, these components generate, in the spectral domain, six power spectral densities (PSDs) and fifteen cross-spectra. The cross-spectra can be split into two groups: a real group and a pure imaginary group. If the source distribution is statistically stationary, 1D spectra can be found from the 2D spectra via the slice theorem. The PSDs form two power-sum rules that link all gradient components. The power-sum rules, in combination with further equalities between the power and cross-spectra, reduce the number of independent spectra to 13, a number reduced to seven if the power spectrum of the potential is assumed isotropic. The power-sum rules, cross-spectral phases, and coherence between components all provide information on the internal consistency of a set of gradiometry measurements. This information can be used to assess the noise, to determine the isotropy, and, for a self-similar source, to calculate the scaling factor and average depth. When applied to a data set collected in the North Sea, the power-sum rules reveal high-frequency noise that is distributed among only three of the gradient components; additionally, the coherences reveal the source to be anisotropic with a nonzero correlation length.
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Chauhan, H., and B. Krishna Mohan. "Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 83–90. http://dx.doi.org/10.5194/isprsannals-ii-8-83-2014.

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The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.
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Xie, Busheng, Wenfei Mao, Boqi Peng, Shengyu Zhou, and Lixin Wu. "Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis." Minerals 12, no. 5 (April 20, 2022): 508. http://dx.doi.org/10.3390/min12050508.

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Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.
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Clegg, Brian. "Spectral Analysis." Impact 2019, no. 1 (January 2, 2019): 12–16. http://dx.doi.org/10.1080/2058802x.2019.1589771.

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Morgan, Michael J. "Spectral analysis." Nature 375, no. 6527 (May 1995): 113–14. http://dx.doi.org/10.1038/375113a0.

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Sinha Roy, Aritro, Boris Dzikovski, Dependu Dolui, Olga Makhlynets, Arnab Dutta, and Madhur Srivastava. "A Simulation Independent Analysis of Single- and Multi-Component cw ESR Spectra." Magnetochemistry 9, no. 5 (April 23, 2023): 112. http://dx.doi.org/10.3390/magnetochemistry9050112.

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The accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understanding their structure–function relationships and electrochemical properties. The current methods of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly/partially resolved and unresolved spectra, which is common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper–nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.
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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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Dissertations / Theses on the topic "Spectral analysis"

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Sendov, Hristo. "Variational Spectral Analysis." Thesis, University of Waterloo, 2000. http://hdl.handle.net/10012/1089.

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We present results on smooth and nonsmooth variational properties of {it symmetric} functions of the eigenvalues of a real symmetric matrix argument, as well as {it absolutely symmetric} functions of the singular values of a real rectangular matrix. Such results underpin the theory of optimization problems involving such functions. We answer the question of when a symmetric function of the eigenvalues allows a quadratic expansion around a matrix, and then the stronger question of when it is twice differentiable. We develop simple formulae for the most important nonsmooth subdifferentials of functions depending on the singular values of a real rectangular matrix argument and give several examples. The analysis of the above two classes of functions may be generalized in various larger abstract frameworks. In particular, we investigate how functions depending on the eigenvalues or the singular values of a matrix argument may be viewed as the composition of symmetric functions with the roots of {it hyperbolic polynomials}. We extend the relationship between hyperbolic polynomials and {it self-concordant barriers} (an extremely important class of functions in contemporary interior point methods for convex optimization) by exhibiting a new class of self-concordant barriers obtainable from hyperbolic polynomials.
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Collins, Brian Harris. "Thermal imagery spectral analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA320553.

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Thesis (M.S. in Systems Technology (Space Systems Operations)) Naval Postgraduate School, September 1996.
Thesis advisor(s): R.C. Olsen, David Cleary. "September 1996." Includes bibliographical references (p. 159-161). Also available online.
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de, Roos Dolf. "Spectral analysis classification sonars." Thesis, University of Canterbury. Electrical Engineering, 1986. http://hdl.handle.net/10092/5575.

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Sonar target classification based on frequency-domain echo analysis is investigated. Conventional pulsed sonars are compared with continuous transmission frequency modulated (CTFM) sonars, and differences relating to target classification are discussed. A practical technique is introduced which eliminates the blind time inherent in CTFM technology. The value and implications of modelling underwater sonars in air are discussed and illustrated. The relative merits of auditory, visual and computer analysis of echoes are examined, and the effects of using two or more analysis methods simultaneously are investigated. Various statistical techniques for detecting and classifying targets are explored. It is seen that with present hardware limitations, a two-stage echo analysis approach offers the most efficient means of target classification. A novel design for three-section quarter-wavelength transducers is presented and evaluated. Their inherently flat frequency response makes these transducers well suited to broadband applications. The design philosophy and construction details of a Diver's Sonar and an underwater Classification Sonar are given. Sea trials reveal that using the Diver's Sonar, a blind-folded diver can successfully navigate in an unknown environment, and locate and classify targets; using the Classification Sonar, targets may be located and classified using either operators or computer software.
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Hu, Zhihua. "Spectral fatigue analysis techniques." Thesis, University College London (University of London), 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362446.

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Cannon, Robert William. "Automated Spectral Identification of Materials using Spectral Identity Mapping." Cleveland State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1377031729.

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Youatt, Andrew Pierce. "Analyzing Edgard Varese's Ionisation Using Digital Spectral Analysis." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/232473.

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Although Robert Cogan's New Images of Musical Sound won the Society of Music Theory's Outstanding Publication Award in 1987, his musical application of spectral analysis has seen little use over the past 25 years. Spectral images are most effective at illustrating the timbre of sound, and harmony, not timbre, is the key structural component of most Western music. There are, however, some compositions in which timbre plays a critical role. Chief among these is Edgard Varèse's Ionisation, an epic percussion ensemble piece built around 40 instruments and 13 musicians. Previous analyses by Jean-Charles François and Varèse protege Chou Wen-Chung have emphasized the importance of timbre to Ionisation's construction, but are limited in their exploration of timbral qualities. Modern digital spectral analysis allows for a more accurate picture of the individual timbres that make up Ionisation and define the broader textures and structures that give the piece meaning.
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Moreira-Paredes, Ramiro. "Nontraditional windows in spectral analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA271336.

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Jamieson, Gary. "Spectral analysis of pulmonary sounds." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240595.

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Bandtlow, Oscar F. "Spectral analysis of dynamical systems." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396095.

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Nastov, Ognen J. (Ognen Jovan). "Spectral methods for circuit analysis." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16718.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 119-124).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Harmonic balance (HB) methods are frequency-domain algorithms used for high accuracy computation of the periodic steady-state of circuits. Matrix-implicit Krylov-subspace techniques have made it possible for these methods to simulate large circuits more efficiently. However, the harmonic balance methods are not so efficient in computing steady-state solutions of strongly nonlinear circuits with rapid transitions. While the time-domain shooting-Newton methods can handle these problems, the low-order integration methods typically used with shooting-Newton methods are inefficient when high solution accuracy is required. We first examine possible enhancements to the standard state-of-the-art preconditioned matrix-implicit Krylovsubspace HB method. We formulate the BDF time-domain preconditioners and show that they can be quite effective for strongly nonlinear circuits, speeding up the HB runtimes by several times compared to using the frequency-domain block-diagonal preconditioner. Also, an approximate Galerkin HB formulation is derived, yielding a small improvement in accuracy over the standard pseudospectral HB formulation, and about a factor of 1.5 runtime speedup in runs reaching identical solution error. Next, we introduce and develop the Time-Mapped Harmonic Balance method (TMHB) as a fast Krylov-subspace spectral method that overcomes the inefficiency of standard harmonic balance for circuits with rapid transitions. TMHB features a non-uniform grid and a time-map function to resolve the sharp features in the signals. At the core of the TMHB method is the notion of pseudo Fourier approximations. The rapid transitions in the solution waveforms are well approximated with pseudo Fourier interpolants, whose building blocks are complex exponential basis functions with smoothly varying frequencies. The TMHB features a matrix-implicit Krylov-subspace solution approach of same complexity as the standard harmonic balance method. As the TMHB solution is computed in a pseudo domain, we give a procedure for computing the real Fourier coefficients of the solution, and we also detail the construction of the time-map function. The convergence properties of TMHB are analyzed and demonstrated on analytic waveforms. The success of TMHB is critically dependent on the selection of a non-uniform grid. Two grid selection strategies, direct and iterative, are introduced and studied. Both strategies are a priori schemes, and are designed to obey accuracy and stability requirements. Practical issues associated with their use are also addressed. Results of applying the TMHB method on several circuit examples demonstrate that the TMHB method achieves up to five orders of magnitude improvement in accuracy compared to the standard harmonic balance method. The solution error in TMHB decays exponentially faster than the standard HB method when the size of the Fourier basis increases linearly. The TMHB method is also up to six times faster than the standard harmonic balance method in reaching identical solution accuracy, and uses up to five times less computer memory. The TMHB runtime speedup factor and storage savings favorably increase for stricter accuracy requirements, making TMHB well suited for high accuracy simulations of large strongly nonlinear circuits with rapid transitions.
by Ognen J. Nastov.
Ph.D.
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Books on the topic "Spectral analysis"

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Castani, Francis, ed. Spectral Analysis. London, UK: ISTE, 2006. http://dx.doi.org/10.1002/9780470612194.

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Cecconi, Jaures, ed. Spectral Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-10955-3.

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service), SpringerLink (Online, ed. Spectral Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Castanié, Francis, ed. Digital Spectral Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118601877.

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Statistical spectral analysis. Englewood Cliffs (N.J.): Prentice Hall, 1988.

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International, Conference on Spectral Line Shapes (14th 1998 State College Pennsylvania). Spectral line shapes. Woodbury, New York: American Institute of Physics, 1999.

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Albin, Pierre, Dmitry Jakobson, and Frédéric Rochon, eds. Geometric and Spectral Analysis. Providence, Rhode Island: American Mathematical Society, 2014. http://dx.doi.org/10.1090/conm/630.

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Functional analysis: Spectral theory. Basel: Birkhäuser Verlag, 1997.

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Wang, Yanwei, Jian Li, and Petre Stoica. Spectral Analysis of Signals. Cham: Springer International Publishing, 2005. http://dx.doi.org/10.1007/978-3-031-02525-9.

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Janas, Jan, Pavel Kurasov, Ari Laptev, Sergei Naboko, and Günter Stolz, eds. Spectral Theory and Analysis. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-7643-9994-8.

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Book chapters on the topic "Spectral analysis"

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Salinger, D. L., and J. D. Stegeman. "Spectral synthesis and difference spectra." In Harmonic Analysis, 265–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0086607.

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Baltz, Andreas, and Lasse Kliemann. "Spectral Analysis." In Network Analysis, 373–416. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_14.

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Muscat, Joseph. "Spectral Theory." In Functional Analysis, 307–43. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06728-5_14.

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Pedersen, Gert K. "Spectral Theory." In Analysis Now, 127–89. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1007-8_4.

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Muscat, Joseph. "Spectral Theory." In Functional Analysis, 345–82. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-27537-1_14.

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Léna, Pierre. "Spectral Analysis." In Astronomy and Astrophysics Library, 274–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-02554-3_7.

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Angermann, Lutz, and Vasyl V. Yatsyk. "Spectral Analysis." In Resonant Scattering and Generation of Waves, 77–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96301-3_4.

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Mudelsee, Manfred. "Spectral Analysis." In Atmospheric and Oceanographic Sciences Library, 169–215. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04450-7_5.

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Brockwell, Peter J., and Richard A. Davis. "Spectral Analysis." In Introduction to Time Series and Forecasting, 97–119. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29854-2_4.

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Mudelsee, Manfred. "Spectral Analysis." In Atmospheric and Oceanographic Sciences Library, 177–227. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9482-7_5.

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Conference papers on the topic "Spectral analysis"

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Preppernau, B. L., and P. J. Hargis. "Trace Organic Chemical Detection Using an Ultraviolet Excitation Molecular Beam Fluorometer." In Laser Applications to Chemical Analysis. Washington, D.C.: Optica Publishing Group, 1994. http://dx.doi.org/10.1364/laca.1994.tub.5.

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Detection of air-borne environmental contaminants, such as organic solvents, requires unambiguous compound identification and sensitivity to concentrations below those permitted by regulating agencies. One promising detection approach uses a pulsed supersonic molecular beam vacuum expansion in combination with fluorescence signal spectral analysis to identify species in a chemical mixture. Expanding a contaminated atmospheric sample through a supersonic molecular beam expansion acts to cool the sample and greatly reduce the spectral density in a fluorescence or photoionization spectrum. Most organic contaminants of interest have electronic transitions in the ultraviolet with near-featureless broad band fluorescence spectra when recorded at atmospheric pressure and room temperature. By using a supersonic vacuum expansion, cooling to within a few degrees of absolute zero can reduce the effective rotational and translational temperatures of the sample molecules and provide a sharply defined spectra which can be used to unambiguously identify specific molecules and their concentrations.
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Pesce, Celso P., Andre´ L. C. Fujarra, and Leonardo K. Kubota. "The Hilbert-Huang Spectral Analysis Method Applied to VIV." In 25th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/omae2006-92119.

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Vortex-Induced Vibration (VIV) is a highly nonlinear dynamic phenomenon. Usual spectral analysis methods rely on the hypotheses of linear and stationary dynamics. A new method envisaged to treat nonlinear and non-stationary signals was presented by Huang et al. [1] : The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This technique, called thereafter the Hilbert-Huang transform (or spectral analysis) method, is here applied to VIV phenomena, aiming at disclosing some hidden dynamic characteristics, such as the time-modulation and jumps of multi-branched response frequencies and their related energy spectra.
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Dai, Bin, Christopher Jones, Jimmy Price, Darren Gascooke, and Anthony Van Zuilekom. "COMPRESSIVE SENSING BASED OPTICAL SPECTROMETER FOR DOWNHOLE FLUID ANALYSIS." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0112.

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Downhole fluid analysis has the potential to resolve ambiguity in very complex reservoirs. Downhole fluid spectra contain a wealth of information to fingerprint a fluid and help to assess continuity. Commonly, a narrowband spectrometer with limited number of channels is used to acquire optical spectra of downhole fluid. The spectral resolution of this type of spectrometer is low due to limited number of narrowband channels. In this paper, we demonstrate a new type, compressive sensing (CS) based broadband spectrometer that provides accurate and high-resolution spectral measurement. Several specially designed broadband filters are used to simplify the mechanical, electrical, optical, and computational construction of a spectrometer, therefore provides measurement of fluid spectrum with high signal-to-noise ratio, robustness, and a broader spectral range. The compressive sensing spectrometer relies on reconstruction technique to compute the optical spectrum. Based on a large spectral database, containing more than 10000 spectra of various fluids at different temperature and pressure conditions, which were collected using conventional high resolution spectrometer in a lab, the basis functions of the optical spectra of three types of fluids (water, oil and gas/condensate) can be extracted. The reconstruction algorithm first classifies the fluid into one of three fluid types based on multichannel CS spectrometer measurements, the optical spectrum is reconstructed by using linear combination of the basis functions of corresponding fluid type, with weighting coefficients determined by minimizing the difference between calculated detector responses and measured detector responses across multiple optical channels. The reconstructed data may then be used for purposes such as contamination measurement, fluid property trends for reservoir continuity assessment, and digital sampling. Digital sampling is the process of extrapolating clean fluid properties from formation fluids not physically sampled. The reconstruction spectrum covers wavelengths from 500 nm to 3300 nm, which is a wider spectral region than has historically been accessible to formation testers. The expanded wavelength range allows access of the mid-infrared spectral region for which synthetic drilling-fluid components typically have higher optical absorbance. This reconstruction spectra may allow contamination to be directly determined. This paper will discuss the CS optical spectrometer design, fluid classification and spectral reconstruction algorithm. In addition, the applicability of the technique to fluid continuity assessment, sample contamination assessment and digital sampling will also be discussed.
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Guarino, Lori A. "Methods of optical spectral analysis." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.wg8.

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Optical spectrum analyzers are convenient instruments for testing parameters such as the emission spectrum of laser diodes or light emitting diodes, the deterioration of the laser diode spectral width due to phenomena such as chirping, and the presence of side modes when measuring single-longitudinal- mode laser diodes. A comparison of optical spectral analysis methods including interference spectrophotometric methods and dispersion spectrophotometric methods is given. Emphasis is placed on diffraction grating and Fabry-Perot inteferometer techniques. The importance of parameters such as dynamic range, resolution, and elimination of effects of polarization is discussed. Examples are given of the use of spectrum analyzers to measure the effects of changes in temperature and current as well as the effects of phenomena such as mode hopping and chirping.
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"Workshop on Higher-order Spectral Analysis." In Workshop on Higher-Order Spectral Analysis. IEEE, 1989. http://dx.doi.org/10.1109/hosa.1989.735258.

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Peterson, Kelly A., Timothy J. Johnson, Bruce E. Bernacki, Charmayne E. Lonergan, and Tanya L. Myers. "Measuring the Optical Constants for Adaptable Spectral Libraries." In Laser Applications to Chemical, Security and Environmental Analysis. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/lacsea.2022.lm3b.3.

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To support stand-off detection, spectral libraries are being developed that include the optical constants, n(ν) and k(ν). Variable angle spectroscopic ellipsometry and single-angle reflectance are used to derive these data, enabling the reference spectra to be modeled for different parameters.
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Ma, Rujian, Guixi Li, and Dong Zhao. "Spectral Analysis of Nonlinear Random Wave Loadings." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67287.

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The spectral analysis of nonlinear random wave loadings on circular cylinders is performed in this paper by means of nonlinear spectral analysis. The study is carried out by expressing the wave profile and velocities of water particles as a nonlinear composition of the first order wave profile. Under the assumption of the first order wave profile being a zero-mean Gaussian process, the random wave spectra of finite amplitude waves are given. In order to solve the loading spectra of the finite amplitude random waves, the drag force is extended into power series of velocity. The loadings of the finite amplitude random waves are then expressed as nonlinear compositions of the first order wave profile and its derivatives. These techniques made it easier to compute the spectral densities of the finite amplitude random wave loadings.
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Jacques, Steven L. "Rapid Spectral Analysis for Spectral Imaging." In Biomedical Optics. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/biomed.2010.bme2.

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Li, Qianqian, Fangfang Wang, and Wenfei Luo. "A PROSAIL-based spectral unmixing algorithm for solving vegetation spectral variability problem." In Multispectral Image Processing and Analysis. SPIE, 2018. http://dx.doi.org/10.1117/12.2283462.

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Scragg, Carl A. "Spectral Analysis of Ship-Generated Waves in Finite-Depth Water." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28510.

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Recent efforts to compare the waves generated by different vessels traveling in finite-depth water have struggled with difficulties presented by various data sets of wave elevations (either measurements or predictions) corresponding to different lateral distances from the ship. Some of the attempts to shift the data to a common reference location have relied upon crude and potentially misleading approximations. The use of free-wave spectral-methods not only overcomes such difficulties, but it also provides us the means to accurately extend CFD results into the far field. As in the deep-water case, one can define a free-wave spectrum that is valid for all lateral positions and distances astern of the vessel. The free-wave spectrum contains a complete description of the Kelvin wake, and wave elevations at any far-field position can be readily calculated once the spectrum is known. For the case of infinitely deep water, Eggers, Sharma, and Ward [1967] presented a method by which free-wave spectra can be determined from appropriate measurements of the far-field wave elevations. The current paper discusses the use of free-wave spectra for finite-depth problems and presents a method for the determination of free-wave spectra based upon fitting predicted wave elevations to a corresponding data set. The predicted wave elevations can be calculated from an unknown distribution of finite-depth Havelock singularities. The unknown singularities are determined by minimizing the mean-square-difference between predicted and measured wave fields. The method appears to be quite general and can be used to calculate either finite or infinite-depth free-wave spectra from experimental data or from local CFD predictions.
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Reports on the topic "Spectral analysis"

1

Georgiou, Tryphon T. High Resolution Spectral Analysis. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada534538.

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Hess, David E. Spectral analysis on a PC. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4733.

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Wilson, Gary R., and Keith R. Hardwicke. Nonstationary Higher Order Spectral Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada246580.

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Hippenstiel, Ralph. Analysis Using Bi-Spectral Related Technique. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada276017.

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Canavan, G., J. Moses, and R. Smith. Hyper-spectral scanner design and analysis. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/249251.

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Parzen, Emanuel. Stationary Time Series Analysis Using Information and Spectral Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada257279.

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Kumar, Akshat. Spectral Methods in Time-dependent Data Analysis. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1489620.

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Coburn, William O. Spectral Analysis of Pulse-Modulated rf Signals. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada369180.

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Georgiou, Tryphon T. Tools For Multivariable Spectral and Coherence Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada567506.

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Santosa, Fadil. Spectral Analysis on the Canonical Autoregressive Decomposition. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada270083.

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