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Journal articles on the topic 'Spectrum analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

Zhang, Lian Shun, and Ai Juan Shi. "Classification of Biological Spectrum Based on Principal Component Cluster Analysis." Advanced Materials Research 605-607 (December 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, and Juergen Hasch. "'Spectrum': Spectral Analysis in Python." Journal of Open Source Software 2, no. 18 (October 27, 2017): 348. http://dx.doi.org/10.21105/joss.00348.

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3

Harrar, Khaled, and Mohamed Khider. "Texture Analysis Using Multifractal Spectrum." International Journal of Modeling and Optimization 4, no. 4 (August 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 (February 1, 1998): 98–99. http://dx.doi.org/10.1093/bioinformatics/14.1.98.

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5

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

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6

Long, Junbo, Haibin Wang, and Peng Li. "Applications of Fractional Lower Order Frequency Spectrum Technologies to Bearing Fault Analysis." Mathematical Problems in Engineering 2019 (August 27, 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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7

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

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8

Chu, Jifeng, Fang-Fang Liao, Stefan Siegmund, Yonghui Xia, and Hailong Zhu. "Nonuniform dichotomy spectrum and reducibility for nonautonomous difference equations." Advances in Nonlinear Analysis 11, no. 1 (July 24, 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

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

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10

Arutyunyan, Rafael, Yuri Obukhov, and Petr Vabishchevich. "NUMERICAL SIMULATION OF CHARGED FULLERENE SPECTRUM." Mathematical Modelling and Analysis 24, no. 2 (March 18, 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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11

LASISI, HAMMED OYEBAMIJI, OLAJIDE MICHEAL ODOFIN, MUHAMMED BABAJIDE HAMMED, and IDOWU OLAMIDE HUSSEIN. "COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK." Journal of Engineering Studies and Research 29, no. 4 (March 7, 2024): 50–56. http://dx.doi.org/10.29081/jesr.v29i4.007.

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Cognitive radio (CR) is an integral system in telecommunications technology that gives unlicensed users access to licensed spectrums via dynamic spectrum access (DSA), to promote spectral efficiency. A significant operation in cognitive radio system is spectrum sensing. This paper evaluates and compares two of the major non-cooperative sensing techniques (energy and cyclostationary feature detector (CFD)) in order, to determine which gives better performance. Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels.

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12

Huu-Tai, Pierre Chau, and Bernard Ducomet. "Energy-dependent Hamiltonian in a nuclear optical model." Electronic Journal of Differential Equations 2021, no. 01-104 (May 4, 2021): 36. http://dx.doi.org/10.58997/ejde.2021.36.

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We study the spectral properties of a 1D model of optical potential introduced by Morillon and Romain [21] in the context of nuclear reactions. We can localize the discrete spectrum and estimate the number of eigenvalues. We also study the continuous spectrum with possibly embedded spectral singularities and give an expansion formula for an arbitrary function on a set of generalized eigenfunctions. We briefly study the resonances of a related model. For more information see https://ejde.math.txstate.edu/Volumes/2021/36/abstr.html

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13

Jordanger, Lars Arne, and Dag Tjøstheim. "Local Gaussian Cross-Spectrum Analysis." Econometrics 11, no. 2 (April 21, 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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14

Zilhadia, Zilhadia, Y. Anggraeni, Y. F. Apriyanti, M. Mustafidah, and I. Jaswir. "Analysis of lard in cod liver oil emulsion using FTIR spectroscopy combined principal component analysis." Food Research 8, no. 3 (June 16, 2024): 424–31. http://dx.doi.org/10.26656/fr.2017.8(3).329.

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Cod liver oil (CLO) contains essential fatty acids and has a high price in the market. Therefore, CLO can be targeted for adulteration to increase economic profits. One of the substances used for adulteration of CLO is lard. This study aimed to analyze the spectral profile of lard and CLO in cod liver oil emulsion using Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA). Lard and CLO were mixed with a range of concentrations of 0-100% used as the control. CLO emulsions were made with a composition of lard and CLO in concentrations similar to those of the control. To analyze the lard and CLO spectrum, lard and CLO were extracted from the emulsion using liquid-liquid extraction, and then the oil extracted from the emulsion was analyzed using FTIR spectroscopy and PCA. Fourier transform infrared spectrum absorption at region 1117–1098 cm-1 is specific to identifying the different spectrums of CLO and lard. The results of PCA showed that clustering between similar oil content and a combination of FTIR and PCA is effective in distinguishing the spectrum of lard and CLO in cod liver oil emulsion.

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15

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

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16

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

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17

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

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18

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

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19

Kume, Kenji, and Naoko Nose-Togawa. "Additive Decomposition of Power Spectrum Density in Singular Spectrum Analysis." Advances in Data Science and Adaptive Analysis 08, no. 01 (January 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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20

Bai, Wurichaihu, Qingmei Bai, and Alatancang Chen. "Upper Semi-Weyl and Upper Semi-Browder Spectra of Unbounded Upper Triangular Operator Matrices." Journal of Function Spaces 2018 (November 25, 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

Fernández-Ramírez, C., L. Muñoz, A. Relaño, and 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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22

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

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23

HEGGE, BRUCE J., and GERHARD MASSELINK. "SPECTRAL ANALYSIS OF GEOMORPHIC TIME SERIES: AUTO-SPECTRUM." Earth Surface Processes and Landforms 21, no. 11 (November 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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24

Cui, Wei Ping, and Xiu Yan Wang. "The Frequency Domain Analysis of Speech Signals Based on MATLAB." Applied Mechanics and Materials 513-517 (February 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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25

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

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26

Liu, Li, and Zhang. "Analysis of Offshore Structures Based on Response Spectrum of Ice Force." Journal of Marine Science and Engineering 7, no. 11 (November 14, 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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27

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

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28

Zhang, Yan Li. "Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique." Applied Mechanics and Materials 40-41 (November 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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29

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

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30

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

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31

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

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32

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

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33

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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34

Huang, Xu Fang, and Jing Kai Chen. "GNSS Signal Power Spectrum Density Analysis." Applied Mechanics and Materials 239-240 (December 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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35

Yegorov, S., V. Lutsenko, A. Yegorov, V. Yegorov, and I. Sinelnikov. "SUBPIXEL SCANNING FOR SPECTRUM IMAGE ANALYSIS." Radio physics and radio astronomy 29, no. 1 (2024): 038–45. http://dx.doi.org/10.15407/rpra29.01.038.

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Subject and Purpose. In this paper, an algorithm is developed to improve the spectrum image resolution in the atomic emission spectrum registration using multi-element detectors and taking multiple exposures with image shifting by a fractional part of a pixel. Methods and Methodology. Mathematical modeling techniques are employed in the coordinate representation dictated by the prior information. The modeling results are checked by experiment. Results. The importance of the resolution value of spectral image registration systems and its impact on the quality of the obtained results have been highlighted. The spectral informativeness of the regularly (matrix- or linearly) structured multi-element detectors has been evaluated against the irregular-structure sensors, such as photographic emulsions. As it has been found that the process of pixel size reduction is demanding, an alternative approach through subpixel image shifting has been proposed. Unlike the case of the corresponding instruments implemented as a rule in the frequency domain, here the relevant theoretical problem reduces to the over- determined system of linear equations. The proposed solution algorithm employs the least squares method having regard to the prior data specificity posed by the isolation of the spectral lines and a smooth flow of the background between them. The digital simulation results and the real experimental evidence from the atomic emission spectral analysis have been presented, illustrating the algorithm operation. The experimental research was carried out for one-dimensional spectrum images registered with linear charge-coupling photodetectors. The so gained resolution was twice the spatial resolution of the photodetector. Conclusions. It has been demonstrated that controlled image shifting not only improves the photometry accuracy but also significantly enhances the detector resolution. The general comparisons of the theory with the experiment have proved the feasibility of bringing the proposed methodology into atomic emission analysis.

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36

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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37

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

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38

Qin, Hong Wu, Nian Feng Li, and Chun Feng Li. "Spectral Analysis of Acoustic Emission Signals Using NI Systems." Advanced Materials Research 663 (February 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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39

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

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40

Andreyev, V. G., and 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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41

Du Shan, 杜杉, 张国玉 Zhang Guoyu, 韩欣欣 Han Xinxin, 徐阳 Xu Yang, 迟明波 Chi Mingbo, and 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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42

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

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43

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

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44

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

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45

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

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46

Yongli Zhao, Yongli Zhao, and 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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47

Kramida, Alexander, Alexander N. Ryabtsev, and Peter R. Young. "Revised Analysis of Fe vii." Astrophysical Journal Supplement Series 258, no. 2 (February 1, 2022): 37. http://dx.doi.org/10.3847/1538-4365/ac3a7e.

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Abstract New spectrograms of multiply ionized iron have been recorded and analyzed, targeting the Fe vii spectrum. As a result, several previously unknown spectral lines and energy levels have been identified in this spectrum. These new data have been analyzed together with all previously published laboratory and astrophysical data on this spectrum. The energy levels have been interpreted using parametric calculations with Cowan codes. Radiative transition rates calculated in this work supplemented other previously published calculations in constructing a complete set of recommended transition probabilities. The ionization energy of Fe vii has been redetermined with a fivefold improvement in accuracy. Its new value is 1,007,928(20) cm−1, corresponding to 124.9671(25) eV.

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48

Malm, H. L., H. J. Dixon, L. D. Cass, and 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 (January 1986): 501–6. http://dx.doi.org/10.1016/0168-9002(86)90454-7.

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49

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

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50

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

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