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Articles de revues sur le sujet « Spectrum analysis »

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Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 13 juillet 2023

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1

Zhang, Lian Shun, et Ai Juan Shi. « Classification of Biological Spectrum Based on Principal Component Cluster Analysis ». Advanced Materials Research 605-607 (décembre 2012) : 2245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2245.

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Spectrums of 17 biological tissue phantoms were measured using the fiber-optic spectrometer. Then, the spectrum was preprocessed by multiplicative scatter correction method to devoice the spectrum. Afterwards the features of the spectrum were extracted via principal component analysis. Ultimately, we applied cluster analysis for the spectral features. The results showed that the accumulated credibility of the first 12 spectral principal components was 99.86% for the spectrum after preprocessing; indicating that this spectrum feature extraction might be done in the case of losing no key information. And the results showed that the 17 biological tissue phantoms can be divided into four main categories according their optical features.
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2

Cokelaer, Thomas, et Juergen Hasch. « 'Spectrum' : Spectral Analysis in Python ». Journal of Open Source Software 2, no 18 (27 octobre 2017) : 348. http://dx.doi.org/10.21105/joss.00348.

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3

Harrar, Khaled, et Mohamed Khider. « Texture Analysis Using Multifractal Spectrum ». International Journal of Modeling and Optimization 4, no 4 (août 2014) : 336–41. http://dx.doi.org/10.7763/ijmo.2014.v4.396.

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4

Charleston, M. A. « Spectrum : spectral analysis of phylogenetic data ». Bioinformatics 14, no 1 (1 février 1998) : 98–99. http://dx.doi.org/10.1093/bioinformatics/14.1.98.

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5

Bujunuru, Anitha, et Srinivasulu Tadisetty. « Performance Analysis of Spectrum Sensing Techniques ». Journal of Advanced Research in Dynamical and Control Systems 11, no 0009-SPECIAL ISSUE (25 septembre 2019) : 355–61. http://dx.doi.org/10.5373/jardcs/v11/20192579.

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6

Dobigeon, Nicolas, et Nathalie Brun. « Spectral mixture analysis of EELS spectrum-images ». Ultramicroscopy 120 (septembre 2012) : 25–34. http://dx.doi.org/10.1016/j.ultramic.2012.05.006.

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7

Long, Junbo, Haibin Wang et Peng Li. « Applications of Fractional Lower Order Frequency Spectrum Technologies to Bearing Fault Analysis ». Mathematical Problems in Engineering 2019 (27 août 2019) : 1–24. http://dx.doi.org/10.1155/2019/7641383.

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The traditional spectral analysis method is used to study the characteristics of bearing fault signals in frequency domain, which is reasonable and effective in general cases. However, it is proved that the fault signals have heavy tails in this paper, which are α stable distribution, and 1<α<2, and even the noises belong to α stable distribution. Then the conventional spectral analysis methods degenerate and even fail under α stable distribution environment. Several improved frequency spectral analysis methods are proposed employing fractional lower order covariation or fractional lower order covariance in this paper, including fractional lower order Blackman-Tukey covariation spectrum (FLOBTCS), fractional lower order periodogram covariation spectrum (FLOPCS), and fractional lower order welch covariation spectrum (FLOWCS). In order to suppress side lobe and improve resolution, we present novel fractional lower order autoregression (FLO-AR) and fractional lower order autoregressive moving average (FLO-ARMA) parameter model frequency spectrum methods, and the calculation steps are summarized. The proposed spectrum methods are compared with the existing methods based on second-order statistics under Gaussian and SαS distribution environments, and the results show that the new algorithms have better performance than the traditional methods. Finally, the improved methods are applied to estimate frequency spectrums of the normal and outer race fault signals, and it is demonstrated that they are effective for fault diagnosis.
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8

Chu, Jifeng, Fang-Fang Liao, Stefan Siegmund, Yonghui Xia et Hailong Zhu. « Nonuniform dichotomy spectrum and reducibility for nonautonomous difference equations ». Advances in Nonlinear Analysis 11, no 1 (24 juillet 2021) : 369–84. http://dx.doi.org/10.1515/anona-2020-0198.

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Abstract For nonautonomous linear difference equations, we introduce the notion of the so-called nonuniform dichotomy spectrum and prove a spectral theorem. As an application of the spectral theorem, we prove a reducibility result.
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9

Arutyunyan, Rafael, Yuri Obukhov et Petr Vabishchevich. « NUMERICAL SIMULATION OF CHARGED FULLERENE SPECTRUM ». Mathematical Modelling and Analysis 24, no 2 (18 mars 2019) : 263–75. http://dx.doi.org/10.3846/mma.2019.017.

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The mathematical model of the electron spectrum of a charged fullerene is constructed on the basis of the potential of a charged sphere and the spherically symmetric potential of an uncharged fullerene. The electron spectrum is defined as the solution of the spectral problem for the one-dimensional Schr\"odinger equation. For the numerical solution of the spectral problem, piecewise-linear finite elements are used. The computational algorithm was tested on the analytical solution of the problem of the spectrum of the hydrogen atom. For solution of matrix spectral problems, a free library for solving spectral problems of SLEPc is used. The results of calculations of the electron spectrum of a charged fullerene C60 are presented.
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10

Pedersen, Steen. « Spectral Sets Whose Spectrum Is a Lattice with a Base ». Journal of Functional Analysis 141, no 2 (novembre 1996) : 496–509. http://dx.doi.org/10.1006/jfan.1996.0139.

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11

Jordanger, Lars Arne, et Dag Tjøstheim. « Local Gaussian Cross-Spectrum Analysis ». Econometrics 11, no 2 (21 avril 2023) : 12. http://dx.doi.org/10.3390/econometrics11020012.

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The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations. This makes it possible to detect local structures in univariate time series that look similar to white noise when investigated by the ordinary auto-spectrum. In this paper, the local Gaussian approach is extended to a local Gaussian cross-spectrum for multivariate time series. The local Gaussian cross-spectrum has the desirable property that it coincides with the ordinary cross-spectrum for Gaussian time series, which implies that it can be used to detect non-Gaussian traits in the time series under investigation. In particular, if the ordinary spectrum is flat, then peaks and troughs of the local Gaussian spectrum can indicate nonlinear traits, which potentially might reveal local periodic phenomena that are undetected in an ordinary spectral analysis.
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12

Amouch, M., M. Benharrat et B. Messirdi. « RETRACTED : Spectral mapping theorem for generalized Kato spectrum ». Journal of Mathematical Analysis and Applications 423, no 1 (mars 2015) : 1–9. http://dx.doi.org/10.1016/j.jmaa.2014.09.043.

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13

Retcofsky, H. L. « Spectrum Analysis Discoverer ? » Journal of Chemical Education 80, no 9 (septembre 2003) : 1003. http://dx.doi.org/10.1021/ed080p1003.1.

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14

Patil, S. P., N. R. Phadnis et S. A. Patil. « Power Spectrum Analysis ». IETE Technical Review 17, no 3 (mai 2000) : 119–21. http://dx.doi.org/10.1080/02564602.2000.11416892.

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15

Wu, T., P.-Y. Chen, C.-H. Chen et C.-L. Wang. « Doppler spectrum analysis ». Journal of Bone and Joint Surgery. British volume 94-B, no 3 (mars 2012) : 344–47. http://dx.doi.org/10.1302/0301-620x.94b3.27122.

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16

Kume, Kenji, et Naoko Nose-Togawa. « Additive Decomposition of Power Spectrum Density in Singular Spectrum Analysis ». Advances in Data Science and Adaptive Analysis 08, no 01 (janvier 2016) : 1650003. http://dx.doi.org/10.1142/s2424922x16500030.

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Singular spectrum analysis (SSA) is a nonparametric and adaptive spectral decomposition of a time series. The singular value decomposition of the trajectory matrix and the anti-diagonal averaging lead to a time-series decomposition. In this paper, we propose an novel algorithm for the additive decomposition of the power spectrum density of a time series based on the filtering interpretation of SSA. This can be used to examine the spectral overlap or the admixture of the SSA decomposition. We can obtain insights into the spectral structure of the SSA decomposition which helps us for the proper choice of the window length in the practical application. The relationship to the conventional SSA decomposition of a time series is also discussed.
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17

Fernández-Ramírez, C., L. Muñoz, A. Relaño et J. Retamosa. « Spectral-statistics analysis of the light meson spectrum ». EPJ Web of Conferences 37 (2012) : 04001. http://dx.doi.org/10.1051/epjconf/20123704001.

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18

Wang, Xiaodong, Jialun Dai, Yafei Zhu, Haiyong Zheng et Xiaoyan Qiao. « Spectral saliency via automatic adaptive amplitude spectrum analysis ». Journal of Electronic Imaging 25, no 2 (12 avril 2016) : 023020. http://dx.doi.org/10.1117/1.jei.25.2.023020.

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19

HEGGE, BRUCE J., et GERHARD MASSELINK. « SPECTRAL ANALYSIS OF GEOMORPHIC TIME SERIES : AUTO-SPECTRUM ». Earth Surface Processes and Landforms 21, no 11 (novembre 1996) : 1021–40. http://dx.doi.org/10.1002/(sici)1096-9837(199611)21:11<1021 ::aid-esp703>3.0.co;2-d.

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20

Bai, Wurichaihu, Qingmei Bai et Alatancang Chen. « Upper Semi-Weyl and Upper Semi-Browder Spectra of Unbounded Upper Triangular Operator Matrices ». Journal of Function Spaces 2018 (25 novembre 2018) : 1–5. http://dx.doi.org/10.1155/2018/7871352.

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In this paper, we study the unbounded upper triangular operator matrix with diagonal domain. Some sufficient and necessary conditions are given under which upper semi-Weyl spectrum (resp. upper semi-Browder spectrum) of such operator matrix is equal to the union of the upper semi-Weyl spectra (resp. the upper semi-Browder spectra) of its diagonal entries. As an application, the corresponding spectral properties of Hamiltonian operator matrix are obtained.
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21

Cui, Wei Ping, et Xiu Yan Wang. « The Frequency Domain Analysis of Speech Signals Based on MATLAB ». Applied Mechanics and Materials 513-517 (février 2014) : 2906–9. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2906.

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Frequency domain analysis of speech signal includes spectrum analysis, power spectrum and the inverse spectrum, spectral envelope analysis. The common methods of frequency domain analysis includes bandpass filters , Fourier transform, linear see method etc.. Using MATLAB discusses the short section of the speech signal spectrum, cepstrum and complex spectrum, the pitch and formant simulation results are given.
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22

Baskakov, A. G., et I. A. Krishtal. « Spectral analysis of operators with the two-point Bohr spectrum ». Journal of Mathematical Analysis and Applications 308, no 2 (août 2005) : 420–39. http://dx.doi.org/10.1016/j.jmaa.2004.11.006.

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23

Tikhonov, A. S. « Spectral Components of Operators with Spectrum on a Curve ». Functional Analysis and Its Applications 37, no 2 (avril 2003) : 155–56. http://dx.doi.org/10.1023/a:1024465208838.

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24

Li, Chong, Shujie Li, Zhaoli Liu et Jianzhong Pan. « On the Fucík spectrum ». Journal of Differential Equations 244, no 10 (mai 2008) : 2498–528. http://dx.doi.org/10.1016/j.jde.2008.02.021.

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25

Yang, Rongwei. « Functional spectrum of contractions ». Journal of Functional Analysis 250, no 1 (septembre 2007) : 68–85. http://dx.doi.org/10.1016/j.jfa.2007.05.015.

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26

Davies, E. B. « Decomposing the essential spectrum ». Journal of Functional Analysis 257, no 2 (juillet 2009) : 506–36. http://dx.doi.org/10.1016/j.jfa.2009.01.031.

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27

Alaminos, J., J. Extremera et A. R. Villena. « Approximately spectrum-preserving maps ». Journal of Functional Analysis 261, no 1 (juillet 2011) : 233–66. http://dx.doi.org/10.1016/j.jfa.2011.02.020.

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28

Liu, Li et Zhang. « Analysis of Offshore Structures Based on Response Spectrum of Ice Force ». Journal of Marine Science and Engineering 7, no 11 (14 novembre 2019) : 417. http://dx.doi.org/10.3390/jmse7110417.

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With the development of large-scale offshore projects, sea ice is a potential threat to the safety of offshore structures. The main forms of damage to bottom-fixed offshore structures under sea ice are crushing failure and bending failure. Referred to as the concept of seismic response spectrums, the design response spectrum of offshore structures induced by the crushing and bending ice failure is presented. Selecting the Bohai Sea in China as an example, the sea areas were divided into different ice zones due to the different sea ice parameters. Based on the crushing and bending failure power spectral densities of ice force, a large amount of ice force time-history samples are firstly generated for each ice zone. The time-history of the maximum responses of a series of single degree of freedom systems with different natural frequencies under the ice force are calculated and subsequently, a response spectrum curve is obtained. Finally, by fitting all the response spectrum curves from different samples, the design response spectrum is generated for each ice zone. The ice force influence coefficients for crushing and bending failure are obtained, which can be used to estimate the stochastic sea ice force acting on a structure conveniently in a static way. A comparison of the proposed response spectrum method with the Monte Carlo method by a numerical example shows good agreement.
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29

Huang, Xu Fang, et Jing Kai Chen. « GNSS Signal Power Spectrum Density Analysis ». Applied Mechanics and Materials 239-240 (décembre 2012) : 603–7. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.603.

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The correct analysis of power spectrum density is very critical when assessing the radio frequency compatibility among GPS, Galileo and BD. Among a number of papers on this research, some, however, ignore the fact that short-code would produce line spectrum, and some present derivation results with errors. The above problems, instead, are given close attention by this paper. The paper firstly analyzes the characteristics of power spectral densities of Global Navigation Satellite Systems (GNSS) baseband signals, taking into account the real properties of the signals, such as code length, data rate, code chipping rate and characteristics of spreading code. And then it presents the detail derivation process. To verify the correctness of its results, GPS C/A code signal is taken as an example. The simulation of this research produces three results that include the line spectrum, real PSD and the spectral separation coefficient of C/A code in different data symbol period. It is concluded that the derivation results prove to be correct, and the data symbol period should be regarded as an important parameter of short code when assessing the radio frequency compatibility.
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30

Yuan, Jiangtao, et 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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31

Zhang, Yan Li. « Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique ». Applied Mechanics and Materials 40-41 (novembre 2010) : 91–95. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.91.

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A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.
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32

KUMAR C M, PUNEETH, et Dr MOHAMED HANEEF. « Response Spectrum and Impulse Excitation Analysis of DMAP Container ». Indian Journal of Applied Research 4, no 7 (1 octobre 2011) : 183–84. http://dx.doi.org/10.15373/2249555x/july2014/55.

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33

Yongli Zhao, Yongli Zhao, et Jie Zhang Jie Zhang. « Blocking probability analysis model for flexible spectrum optical networks ». Chinese Optics Letters 12, no 7 (2014) : 070601–70606. http://dx.doi.org/10.3788/col201412.070601.

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34

Lee, UkJae, Jun Woo Bae et Hee Reyoung Kim. « Multiple beta spectrum analysis based on spectrum fitting ». Journal of Radioanalytical and Nuclear Chemistry 314, no 2 (19 août 2017) : 617–22. http://dx.doi.org/10.1007/s10967-017-5409-5.

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35

Vallev, Anvar. « COMBINED VIBRATION AND STRAIN GAUGE ANALYSIS FOR DIAGNOSTICS OF INDUSTRIAL MACHINES ». VOLUME 39, VOLUME 39 (2021) : 38. http://dx.doi.org/10.36336/akustika20213938.

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The Paper is devoted to combined vibration and strain gauge analysis for diagnostics of industrial machines. It is suggested to use strain gauge spectrum analysis. For studying it an experimental installation is designed and created. This installation allows getting signal from strain gauge sensors with maximum frequency 12.87 kHz. According to experimental study application of strain gauge spectrum analysis can provide almost the same information as vibration spectrum analysis. Combination of these two spectrums can give more useful information. Particularly it can be used for filtering noise. In the experimental study spectrums were significantly cleared without any instrumental and digital filters, it was done only by analysis of combination of the spectrums. This study can provide more reliable diagnostics of industrial machines.
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36

Andrejev, V. G., Ngoc L. Tran et Tien P. Nguyen. « Parametric Spectral Analysis of Noisy Signals with Unimodal Spectrum ». Radioelectronics and Communications Systems 62, no 1 (janvier 2019) : 34–41. http://dx.doi.org/10.3103/s0735272719010059.

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37

Andreyev, V. G., et H. L. Tran. « PARAMETRIC SPECTRAL ANALYSIS OF UNIMODAL SPECTRUM FOR NOISY SIGNALS ». Vestnik of Ryazan State Radio Engineering University 59, no 3 (2016) : 3–8. http://dx.doi.org/10.21667/1995-4565-2016-57-3-3-8.

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38

Du Shan, 杜杉, 张国玉 Zhang Guoyu, 韩欣欣 Han Xinxin, 徐阳 Xu Yang, 迟明波 Chi Mingbo et 吴一辉 Wu Yihui. « Design of Wide-Spectrum High-Resolution Spectral Analysis System ». Laser & ; Optoelectronics Progress 56, no 8 (2019) : 083003. http://dx.doi.org/10.3788/lop56.083003.

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39

Katz‐Stone, D. M., et L. Rudnick. « A Spectral Analysis of Two Compact Steep‐Spectrum Sources ». Astrophysical Journal 479, no 1 (10 avril 1997) : 258–67. http://dx.doi.org/10.1086/303882.

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40

Schulz, Michael, et Karl Stattegger. « Spectrum : spectral analysis of unevenly spaced paleoclimatic time series ». Computers & ; Geosciences 23, no 9 (novembre 1997) : 929–45. http://dx.doi.org/10.1016/s0098-3004(97)00087-3.

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41

Malm, H. L., H. J. Dixon, L. D. Cass et J. J. Lipsett. « Real time spectrum analysis ». Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment 242, no 3 (janvier 1986) : 501–6. http://dx.doi.org/10.1016/0168-9002(86)90454-7.

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42

Holmström, Lasse, et Ilkka Launonen. « Posterior singular spectrum analysis ». Statistical Analysis and Data Mining : The ASA Data Science Journal 6, no 5 (8 juillet 2013) : 387–402. http://dx.doi.org/10.1002/sam.11195.

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43

Hou, Jin-Chuan, et Xiu-Ling Zhang. « On the Weyl Spectrum : Spectral Mapping Theorem and Weyl's Theorem ». Journal of Mathematical Analysis and Applications 220, no 2 (avril 1998) : 760–68. http://dx.doi.org/10.1006/jmaa.1997.5897.

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44

Qin, Hong Wu, Nian Feng Li et Chun Feng Li. « Spectral Analysis of Acoustic Emission Signals Using NI Systems ». Advanced Materials Research 663 (février 2013) : 507–10. http://dx.doi.org/10.4028/www.scientific.net/amr.663.507.

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This paper describes a software and hardware analytical complex of spectral characteristics. The analysis complex includes the research of the signals with the ability to create a database for identification. Analysis of the spectral characteristics by using special instruments - spectrum analyzer. Presents the results of the evaluation of scientific and practical significance of the software developed. Spectral data multichannel analyzer spectrum was obtained with DAQ-device National Instruments.
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45

Moskvina, V., et K. M. Schmidt. « Approximate Projectors in Singular Spectrum Analysis ». SIAM Journal on Matrix Analysis and Applications 24, no 4 (janvier 2003) : 932–42. http://dx.doi.org/10.1137/s0895479801398967.

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46

McDonald, Patrick, et Robert Meyers. « Dirichlet spectrum and heat content ». Journal of Functional Analysis 200, no 1 (mai 2003) : 150–59. http://dx.doi.org/10.1016/s0022-1236(02)00076-9.

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Cade, Patrick, et Rongwei Yang. « Projective spectrum and cyclic cohomology ». Journal of Functional Analysis 265, no 9 (novembre 2013) : 1916–33. http://dx.doi.org/10.1016/j.jfa.2013.07.010.

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48

Romanov, Roman, et Harald Woracek. « Canonical systems with discrete spectrum ». Journal of Functional Analysis 278, no 4 (mars 2020) : 108318. http://dx.doi.org/10.1016/j.jfa.2019.108318.

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49

Donnelly, Harold. « Essential spectrum and heat kernel ». Journal of Functional Analysis 75, no 2 (décembre 1987) : 362–81. http://dx.doi.org/10.1016/0022-1236(87)90101-7.

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50

Courtois, G. « Spectrum of Manifolds with Holes ». Journal of Functional Analysis 134, no 1 (novembre 1995) : 194–221. http://dx.doi.org/10.1006/jfan.1995.1142.

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