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Journal articles on the topic 'Spectrum'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 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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Mück, Markus, and Christophe GAIE. "The Future of Spectrum Management - Conciliating Spectrum Allocation, Spectral Efficiency and Spectrum Monetization." International Journal of Computational Systems Engineering 7, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijcsyse.2022.10054832.

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3

Gaie, Christophe, and Markus Mueck. "The future of spectrum management - conciliating spectrum allocation, spectral efficiency and spectrum monetisation." International Journal of Computational Systems Engineering 7, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijcsyse.2022.131031.

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4

He, Wei, De Tian, and Wei Long Wang. "Effect of Wind Shear on Rotational Fourier Spectrum of Wind Turbine." Applied Mechanics and Materials 271-272 (December 2012): 872–76. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.872.

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In order to accurately obtain the influence of rotational effect on fluctuating component of turbulent wind acted on wind turbine, considering source spectrum and rotational sample points were changed since wind shear changed mean wind speed in the rotor plane along the vertical direction, rotational Fourier spectrum with wind shear was deduced. Based on the investigation on a 3MW three-bladed pitch regulated wind turbine, rotational Fourier spectrums with and without wind shear were compared, the effect of radius on rotational Fourier spectrum was analyzed, cross power spectral densities of rotational Fourier spectrum with different blades and variable radius were compared. The results show that the effect of wind shear on amplitude of rotational Fourier spectrum is unconspicuous while the phase frequency characteristic is alternative for the sample points are changed. Rotational Fourier spectrum energy removes to the high frequency bands, the amplitudes of peak at the integer multiple rotational frequencies increase with radius increases. The initial phase has little influence to the amplitude of spectrum, and cross power spectral densities of different radius are obviously less than auto power spectral densities of the same radius.

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5

Wang, Mingxun, and Nuno Bandeira. "Spectral Library Generating Function for Assessing Spectrum-Spectrum Match Significance." Journal of Proteome Research 12, no. 9 (July 31, 2013): 3944–51. http://dx.doi.org/10.1021/pr400230p.

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Yen, Chia-Yu, Stephane Houel, Natalie G. Ahn, and William M. Old. "Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library." Molecular & Cellular Proteomics 10, no. 7 (April 30, 2011): M111.007666. http://dx.doi.org/10.1074/mcp.m111.007666.

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7

Et.al, Tae-Yun Jung. "Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 538–43. http://dx.doi.org/10.17762/turcomat.v12i6.1971.

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This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method

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Wang, Gao, Jian Quan Yao, De Gang Xu, Gao Wang, and Xiao Fang Ren. "Research on Three Fierce Exploders Reflected Terahertz Spectrum." Advanced Materials Research 760-762 (September 2013): 492–96. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.492.

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The rapid development of the Terahertz technology has been the essential focus among all application fields. In order to applying the spectral technology to the security and analyzing all exploders spectrum characteristics, the paper aims to three exploders spectrums researches including the HNS, DNMT and DNAN. The three reflected spectrums are tested and analyzed, using the most advanced portable terahertz spectrometer, and the absorption peaks among 0.1~2.5THz are determined successfully.

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9

Muraleedharan, Thettath K., and Krishnan Parthasarathy. "Difference spectrum and spectral synthesis." Tohoku Mathematical Journal 51, no. 1 (1999): 65–73. http://dx.doi.org/10.2748/tmj/1178224853.

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10

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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11

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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12

Chen, En Wei, Tie Ming Zhou, and Zheng Shi Liu. "Method of Modeling the Excitation Spectrum of Train Based on Track Irregularity." Applied Mechanics and Materials 97-98 (September 2011): 931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.931.

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A new technique is present in this paper that transforms the railway track irregularity power spectral density of left and right rails into the excitation power spectral density of wheelset of train, compared with the railway track irregularity spectrums of left and right rails which are not the direct inputs of simulation dynamic model of trail. A parameter model is chosen as the model of excitation spectrum and parameters fitting result shows that this model is suitable for the excitation spectrums of traversing, floating and head shaking, and the method present in this paper is effective.

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He, Song Hua, Qiao Chen, Gang Zhang, and Jiang Duan. "Comparative Studies of Two Metameric Black Spectral Dimension Reduction Methods Based on Color Difference Optimization." Applied Mechanics and Materials 731 (January 2015): 120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.731.120.

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Two new metameric black spectral dimension reduction methods based on color difference optimization are presented, and dimension reduction effects are compared in colorimetric and spectral accuracy. The method one decomposes firstly the original spectrum into the basic spectrum and the metameric black spectrum using R-matrix theory, and then determines respectively the basis vectors which express linearly the basic spectrum and the metameric black spectrum. The method two applies firstly the principal component method to the original spectrum to get the first three eigenvectors as basis vectors of the basic spectrum, and then calculates the fundamental spectrum using tristimulus values and basis vectors of original spectrum. Results of experiment show the low-dimensional linear model built by method two can improve spectral and colorimetric accuracy, and satisfy the requirement of spectral color reproduction.

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Saha, Rony Kumer. "Realization of Licensed/Unlicensed Spectrum Sharing Using eICIC in Indoor Small Cells for High Spectral and Energy Efficiencies of 5G Networks." Energies 12, no. 14 (July 22, 2019): 2828. http://dx.doi.org/10.3390/en12142828.

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In this paper, we show how to realize numerous spectrum licensing policies by means of time-domain enhanced inter-cell interference coordination (eICIC) technique to share both the licensed and unlicensed spectrums with small cells in order to address the increasing demand of capacity, spectral efficiency, and energy efficiency of future mobile networks. Small cells are deployed only in 3-dimensional (3D) buildings within a macrocell coverage of a mobile network operator (MNO). We exploit the external wall penetration loss of each building to realize traditional dedicated access, co-primary shared access (CoPSA), and licensed shared access (LSA) techniques for the licensed spectrum access, whereas, for the unlicensed spectrum access, the licensed assisted access (LAA) technique operating in the 60 GHz unlicensed band is realized. We consider that small cells are facilitated with dual-band, and derive the average capacity, spectral efficiency, and energy efficiency metrics for each technique. We perform extensive evaluation of various performance metrics and show that LAA outperforms considerably all other techniques concerning particularly spectral and energy efficiencies. Finally, we define an optimal density of small cells satisfying both the spectral efficiency and energy efficiency requirements for the fifth-generation (5G) mobile networks.

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15

Zeng, Jiadong, Zhitian Zhang, Mingshui Li, and Shaopeng Li. "Spectral Characteristics of Fluctuating Aerodynamic Forces Acting on Rectangular Prisms." Applied Sciences 13, no. 21 (November 2, 2023): 11975. http://dx.doi.org/10.3390/app132111975.

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The present work is devoted to the role of boundary layer turbulence on the spectral characteristics of fluctuating wind loads on large aspect-ratio rectangular prisms. Seven rectangular rigid models with different side ratios (1/4~4) were created, and simultaneous pressure experiments were conducted under the boundary layer turbulence flows. Using the measured data, the power spectrums of the fluctuating aerodynamic forces were calculated, and then, the spectral characteristics under different turbulent boundary layer flows were analyzed. In contrast to the typical power spectrum model, the main factors affecting the spectral characteristics of the fluctuating aerodynamic loads are presented and discussed in this study. The power spectrum of the rectangular prism was significantly impacted by the turbulent wind field, primarily because higher turbulence intensity levels result in a lower spectral peak and a wider spectral bandwidth, which also redistributes spectral energy. In particular, the effect on the spectral properties of across-wind fluctuating loads was stronger, and the turbulent disturbance modified the lateral separation flow structure, causing the reattachment phenomenon to occur earlier on rectangular prisms with small side ratios, which effectively altered the spectral properties. Thus, a normalized across-wind fluctuating wind load spectrum model is proposed, taking into account the effects of incoming turbulence, the side ratio, vortex shedding, separation reattachment flow-induced spectral bandwidth changes, and high-frequency fluctuations.

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16

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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17

Upsal, Jeremy, and Bernard Deconinck. "Real Lax spectrum implies spectral stability." Studies in Applied Mathematics 145, no. 4 (September 3, 2020): 765–90. http://dx.doi.org/10.1111/sapm.12335.

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18

Hassler, Susan. "IEEE spectrum gets greener - [spectral lines]." IEEE Spectrum 46, no. 1 (January 2009): 9. http://dx.doi.org/10.1109/mspec.2009.4734295.

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19

Zorpette, Glenn. "Exploring the radio spectrum [Spectral Lines]." IEEE Spectrum 51, no. 11 (November 2014): 10–12. http://dx.doi.org/10.1109/mspec.2014.6934913.

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20

Tamamitsu, Miu, Yutaro Kitagawa, Keiichi Nakagawa, Ryoichi Horisaki, Yu Oishi, Shin-ya Morita, Yutaka Yamagata, Kentaro Motohara, and Keisuke Goda. "Spectrum slicer for snapshot spectral imaging." Optical Engineering 54, no. 12 (December 29, 2015): 1. http://dx.doi.org/10.1117/1.oe.54.12.123115.

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21

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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22

Haddad, M., A. Hayar, and M. Debbah. "Spectral efficiency of spectrum-pooling systems." IET Communications 2, no. 6 (2008): 733. http://dx.doi.org/10.1049/iet-com:20070469.

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23

Lingli, Jiang, Li Shuhui, Li Xuejun, Lei Jiale, and Yang Dalian. "Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network." Measurement Science and Technology 33, no. 4 (January 21, 2022): 045008. http://dx.doi.org/10.1088/1361-6501/ac471a.

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Abstract The transmission paths of vibration signals in the planetary gearboxes are complex. The signals have the characteristics of strong background noise, instability and non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the amplitudes of fault characteristic frequency, or the other further quantitative calculations values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the main information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum + CNN, original vibration signal + CNN, local bi-spectrum + (support vector machines), and local bi-spectrum + (stacked auto-encoder) showed that the proposed method in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

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24

Angeltveit, Vigleik. "Uniqueness of MoravaK-theory." Compositio Mathematica 147, no. 2 (September 27, 2010): 633–48. http://dx.doi.org/10.1112/s0010437x10005026.

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AbstractWe show that there is an essentially uniqueS-algebra structure on the MoravaK-theory spectrumK(n), whileK(n) has uncountably manyMUor$\widehat {E(n)}$-algebra structures. Here$\widehat {E(n)}$is theK(n)-localized Johnson–Wilson spectrum. To prove this we set up a spectral sequence computing the homotopy groups of the moduli space ofA∞structures on a spectrum, and use the theory ofS-algebrak-invariants for connectiveS-algebras found in the work of Dugger and Shipley [Postnikov extensions of ring spectra, Algebr. Geom. Topol.6(2006), 1785–1829 (electronic)] to show that all the uniqueness obstructions are hit by differentials.

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25

Roy, P. C., and J. C. Bhakta. "Effect of finite spectral width on the modulational instability of Alfvén waves." Journal of Plasma Physics 47, no. 3 (June 1992): 521–31. http://dx.doi.org/10.1017/s0022377800024399.

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The effect of finite spectral width on the modulational instability of Alfvén waves described by the derivative nonlinear Schrodinger equation is investigated using a method developed by Alber to derive a transport equation for the spectral density. The dispersion relation for a monochromatic wave is regained for a delta spectrum. It is shown that the growth rate and domain of modulational instability diminish as the spectral width increases for both the Gaussian and uniform spectrums.

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26

Umam, Khoirul. "Spectrum dan Spectrum Laplacian pada Graf Mahkota." KALBISCIENTIA Jurnal Sains dan Teknologi 6, no. 2 (September 28, 2020): 161. http://dx.doi.org/10.53008/kalbiscientia.v6i2.50.

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Crown Crown () is a graph that has the number of vertices and the number of edges are with , integers. Suppose that are eigen values of a matrix and are the multiplicity of each , so the spectrum of a graph can be expressed as a matrix whose line elements are in the first row, and in the second row. In this paper, we will discuss spectrums from adjacency matrices and laplacian matrix spectrums of

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27

Saha, Rony Kumer. "Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks." Energies 12, no. 20 (October 10, 2019): 3825. http://dx.doi.org/10.3390/en12203825.

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In this paper, we propose a technique to share the licensed spectrums of all mobile network operators (MNOs) of a country with in-building small cells per MNO by exploiting the external wall penetration loss of a building and introducing the time-domain eICIC technique. The proposed technique considers allocating the dedicated spectrum Bop per MNO only its to outdoor macro UEs, whereas the total spectrum of all MNOs of the country Bco to its small cells indoor per building such that technically any small indoor cell of an MNO can have access to Bco instead of merely Bop assigned only to the MNO itself. We develop an interference management strategy as well as an algorithm for the proposed technique. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived, and a generic model for energy efficiency is presented. An optimal amount of small indoor cell density in terms of the number of buildings L carrying these small cells per MNO to trade-off the spectral efficiency and the energy efficiency is derived. With the system-level numerical and simulation results, we define an optimal value of L for a dense deployment of small indoor cells of an MNO and show that the proposed spectrum sharing technique can achieve massive indoor capacity, spectral efficiency, and energy efficiency for the MNO. Finally, we demonstrate that the proposed spectrum sharing technique could meet both the spectral efficiency and the energy efficiency requirements for 5G mobile networks for numerous traffic arrival rates to small indoor cells per building of an MNO.

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Zhang, Li Zheng, and Jin Cheng Li. "Ink Spectrum Data Feature Extraction Research." Applied Mechanics and Materials 731 (January 2015): 87–91. http://dx.doi.org/10.4028/www.scientific.net/amm.731.87.

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In the field of printing research, color replication based on spectrum need to solve the issues that how to get cyan, magenta and yellow ink spectrum feature extraction. In this study, based on spectral derivative and spectral image deviation, establish a method to choose special wavelength positions for making up the corresponding relation between ink value and ink spectral reflectivity. The ink scales are printed on papers in the way that the value of the ink dot increase 5% from 0-100%. Ink spectrum reflectivity curves are made up through measuring ink scales. The first-order derivatives of cyan, magenta and yellow ink spectrum reflectivity curves in the range of 380-730nm, in sequence, change smooth at 650nm, 530-560nm, 450nm, and change violently at 530nm, 600nm, 500nm wavelength position. Based on spectral image deviation, the cyan ink spectral wavelength in the range of 630-730nm, the magenta ink spectral wavelength in the range of 500-540nm, include the most information and the least correlation. For the reason that yellow ink spectral images intersect with others, the analysis is combined with first-order derivative of ink spectrum curves. The method proposed in this paper is propitious to get primary ink spectral feature for color spectrum separation.

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29

Bashir, Bisma, and Er Tajinder Pal Singh. "Spectrum Handoff Decisions." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 36–38. http://dx.doi.org/10.31142/ijtsrd18404.

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30

Dong, W. P., and K. J. Stout. "Two-Dimensional Fast Fourier Transform and Power Spectrum for Surface Roughness in three Dimensions." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 209, no. 5 (October 1995): 381–91. http://dx.doi.org/10.1243/pime_proc_1995_209_097_02.

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Two-dimensional power spectrums of engineering surfaces contain plenty of information that is important and valuable for surface characterization. However, the characteristics of the two-dimensional spectrums are largely unknown and the algorithm to implement them is not familiar to many engineers or researchers. This paper describes a detailed procedure to implement the two-dimensional fast Fourier transform and power spectrum for surface roughness in three dimensions. Methods used to extract information from the spectrums are introduced. In order to perform two-dimensional spectral analysis and to have a comprehensive understanding of the characteristics of engineering surfaces, an atlas of the two-dimensional spectrums of representative engineering surfaces are presented. The properties of the spectrums are discussed in conjunction with theoretical analysis and visual characterization of the presented spectrums.

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31

Ying Wang, Ying Wang, Jie Zhang Jie Zhang, Yongli Zhao Yongli Zhao, Junyan Liu Junyan Liu, and Wanyi Gu Wanyi Gu. "Spectrum consecutiveness based routing and spectrum allocation in flexible bandwidth networks." Chinese Optics Letters 10, s1 (2012): S10606–310609. http://dx.doi.org/10.3788/col201210.s10606.

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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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33

Mouton, Sonja. "Generalized spectral perturbation and the boundary spectrum." Czechoslovak Mathematical Journal 71, no. 2 (February 2, 2021): 603–21. http://dx.doi.org/10.21136/cmj.2021.0046-20.

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Kerekes, J. P., and J. E. Baum. "Full-spectrum spectral imaging system analytical model." IEEE Transactions on Geoscience and Remote Sensing 43, no. 3 (March 2005): 571–80. http://dx.doi.org/10.1109/tgrs.2004.841428.

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35

González, P., J. Vijande, A. Valcarce, and H. Garcilazo. "Spectral patterns in the nonstrange-baryon spectrum." European Physical Journal A 29, no. 2 (August 2006): 235–44. http://dx.doi.org/10.1140/epja/i2006-10081-1.

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Ruddick, Barry, Ayal Anis, and Keith Thompson. "Maximum Likelihood Spectral Fitting: The Batchelor Spectrum." Journal of Atmospheric and Oceanic Technology 17, no. 11 (November 2000): 1541–55. http://dx.doi.org/10.1175/1520-0426(2000)017<1541:mlsftb>2.0.co;2.

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37

Zorpette, Glenn. "Famous bylines in IEEE Spectrum [Spectral Lines]." IEEE Spectrum 51, no. 8 (August 2014): 8–11. http://dx.doi.org/10.1109/mspec.2014.6866421.

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38

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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Yoo, Jong-Kwang. "THE SPECTRAL MAPPING THEOREM FOR LOCALIZABLE SPECTRUM." Far East Journal of Mathematical Sciences (FJMS) 100, no. 3 (July 30, 2016): 491–504. http://dx.doi.org/10.17654/ms100030491.

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40

Çeken, Seçil, and Mustafa Alkan. "Second Spectrum of Modules and Spectral Spaces." Bulletin of the Malaysian Mathematical Sciences Society 42, no. 1 (February 1, 2017): 153–69. http://dx.doi.org/10.1007/s40840-017-0473-0.

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41

Gursky, Sharon. "Sociality in the spectral tarsier,Tarsius spectrum." American Journal of Primatology 51, no. 1 (May 2000): 89–101. http://dx.doi.org/10.1002/(sici)1098-2345(200005)51:1<89::aid-ajp7>3.0.co;2-7.

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Ding, Zhenming, Zhangqi Dang, Xinhong Jiang, and Ziyang Zhang. "Solar spectrum measured by waveguide spectral lens." Optics & Laser Technology 169 (February 2024): 110110. http://dx.doi.org/10.1016/j.optlastec.2023.110110.

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43

Lee, Seokjun, Seung-Won Jung, and Hyunseok Seo. "Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 2929–37. http://dx.doi.org/10.1609/aaai.v38i4.28074.

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Currently, image generation and synthesis have remarkably progressed with generative models. Despite photo-realistic results, intrinsic discrepancies are still observed in the frequency domain. The spectral discrepancy appeared not only in generative adversarial networks but in diffusion models. In this study, we propose a framework to effectively mitigate the disparity in frequency domain of the generated images to improve generative performance of both GAN and diffusion models. This is realized by spectrum translation for the refinement of image generation (STIG) based on contrastive learning. We adopt theoretical logic of frequency components in various generative networks. The key idea, here, is to refine the spectrum of the generated image via the concept of image-to-image translation and contrastive learning in terms of digital signal processing. We evaluate our framework across eight fake image datasets and various cutting-edge models to demonstrate the effectiveness of STIG. Our framework outperforms other cutting-edges showing significant decreases in FID and log frequency distance of spectrum. We further emphasize that STIG improves image quality by decreasing the spectral anomaly. Additionally, validation results present that the frequency-based deepfake detector confuses more in the case where fake spectrums are manipulated by STIG.

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

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

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Trigka, Maria, and Elias Dritsas. "An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks." Sensors 22, no. 17 (September 4, 2022): 6692. http://dx.doi.org/10.3390/s22176692.

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The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs’ observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters’ vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency.

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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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Li, Jian, Ying Xu, Xinhai Zou, Junfeng Zhu, Zhongtao Ruan, Yali Zhang, Zhiyao Zhang, Shangjian Zhang, and Yong Liu. "Resolving Intrinsic Modulation Spectral Lines from Electro-Optic Modulation Spectra Based on Boosted Iterative Deconvolution." Photonics 9, no. 11 (November 20, 2022): 878. http://dx.doi.org/10.3390/photonics9110878.

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Optical spectrum analysis is the most direct and effective method for characterizing electro-optic modulation spectra. According to the Rayleigh criterion, the best resolution of an optical spectrum analysis is limited by the point spread function of an optical spectrum analyzer (OSA) and the relative intensity of closely spaced spectral lines. In this paper, we propose a boosted iterative deconvolution (BID) method to resolve the intrinsic modulation of spectral lines from the measured optical carrier and modulation spectra. In our scheme, the electro-optic modulation spectrum is considered the convolution of the optical carrier spectrum and the intrinsic δ-function modulation spectrum, and the BID method enables fast and accurate extraction of the δ-function spectral lines from the measured modulation spectrum. The proof-of-concept experiment demonstrates that our method can improve the resolution of OSA by 10–30 p.m. at different relative intensities, with a best resolution of 10 p.m. in the iso-intensity case and wavelength errors of less than 2 p.m., which largely improves the measurement resolution and accuracy of the modulation spectrum.

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Mbekhta, Mostafa. "Local spectrum and generalized spectrum." Proceedings of the American Mathematical Society 112, no. 2 (February 1, 1991): 457. http://dx.doi.org/10.1090/s0002-9939-1991-1045142-x.

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Zhao, John X., Mingxiu Jiang, Xiaonan Zhang, and Lili Kang. "A Damping Modification Factor for Horizontal Acceleration Spectrum from Subduction Slab Earthquakes in Japan Accounting for Site Conditions." Bulletin of the Seismological Society of America 110, no. 4 (May 26, 2020): 1942–59. http://dx.doi.org/10.1785/0120190242.

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ABSTRACT We present a damping modification factor (DMF) model for the total acceleration spectrum from subduction slab earthquakes. The model can be used for scaling a 5% damped design spectrum not associated with a particular earthquake that occurred in a subduction slab. The DMF model uses site-period-based site classes as the site-effect proxy. DMF models were constructed based on the spectrum for 13 damping ratios and 34 spectral periods; the DMF values can be calculated for any damping ratio between 1% and 30% and at any spectral period between 0.03 and 5.0 s. At moderately long and long spectral periods, the DMF values for acceleration spectrum are similar to or less than those for the displacement spectrum for a damping ratio of less than 5%, whereas the DMF values for the acceleration spectrum are similar to or larger than those for the displacement spectrum when the damping ratio is more than 5%. The standard deviations for acceleration and displacement spectra are similar at short or moderately short spectral periods, but those for the acceleration spectrum are about twice those for the displacement spectrum at long spectral periods. All standard deviations decrease linearly with increasing damping ratios in the logarithm scale when the damping ratio is less than 5% and increase linearly with increasing damping ratios in a logarithm scale for the other damping ratios. A set of simple functions for calculating various standard deviations is presented. The spectra from the Zhao, Jiang, et al. (2016) study for slab events scaled by the DMF values for other damping ratios vary smoothly with spectral period and have a trough at short spectral periods for a large event, a short distance, and high damping ratios. The relatively large between-event and within-site standard deviations are from the source and path effects.

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Abudayah, Mohammad, Omar Alomari, and Torsten Sander. "On the N-spectrum of oriented graphs." Open Mathematics 18, no. 1 (June 4, 2020): 486–95. http://dx.doi.org/10.1515/math-2020-0167.

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Abstract Given any digraph D, its non-negative spectrum (or N-spectrum, shortly) consists of the eigenvalues of the matrix AA T , where A is the adjacency matrix of D. In this study, we relate the classical spectrum of undirected graphs to the N-spectrum of their oriented counterparts, permitting us to derive spectral bounds. Moreover, we study the spectral effects caused by certain modifications of a given digraph.

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