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Journal articles on the topic 'Multiscale Data Analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Gilbert, A. C. "Multiscale Analysis and Data Networks." Applied and Computational Harmonic Analysis 10, no. 3 (2001): 185–202. http://dx.doi.org/10.1006/acha.2000.0342.

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2

Lessmann, Birgit, Tim W. Nattkemper, Preminda Kessar, et al. "Multiscale analysis of MR-mammography data." Zeitschrift für Medizinische Physik 17, no. 3 (2007): 166–71. http://dx.doi.org/10.1016/j.zemedi.2006.11.009.

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3

Zhao, Tongzhou, Liang Wu, Dehua Li, and Yiming Ding. "Multifractal Analysis of Hydrologic Data Using Wavelet Methods and Fluctuation Analysis." Discrete Dynamics in Nature and Society 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3148257.

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We study the multifractal properties of water level with a high-frequency and massive time series using wavelet methods (estimation of Hurst exponents, multiscale diagram, and wavelet leaders for multifractal analysis (WLMF)) and multifractal detrended fluctuation analysis (MF-DFA). The dataset contains more than two million records from 10 observation sites at a northern China river. The multiscale behaviour is observed in this time series, which indicates the multifractality. This multifractality is detected via multiscale diagram. Then we focus on the multifractal analysis using MF-DFA and

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4

Xie, Shengkun, Anna T. Lawniczak, Sridhar Krishnan, and Pietro Lio. "Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification." ISRN Computational Mathematics 2012 (July 29, 2012): 1–13. http://dx.doi.org/10.5402/2012/197352.

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We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method

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5

Romero, David, Roger Orellana, and María Engracia Hernández-Cerda. "Multiscale spatial variographic analysis of hydroclimatic data." Theoretical and Applied Climatology 144, no. 1-2 (2021): 55–66. http://dx.doi.org/10.1007/s00704-020-03513-9.

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6

Archibald, Nick, Paul Gow, and Fabio Boschetti. "Multiscale edge analysis of potential field data." Exploration Geophysics 30, no. 1-2 (1999): 38–44. http://dx.doi.org/10.1071/eg999038.

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7

Riedl, M., N. Marwan, and J. Kurths. "Multiscale recurrence analysis of spatio-temporal data." Chaos: An Interdisciplinary Journal of Nonlinear Science 25, no. 12 (2015): 123111. http://dx.doi.org/10.1063/1.4937164.

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8

Thuraisingham, Ranjit A., and Georg A. Gottwald. "On multiscale entropy analysis for physiological data." Physica A: Statistical Mechanics and its Applications 366 (July 2006): 323–32. http://dx.doi.org/10.1016/j.physa.2005.10.008.

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9

Øigård, Tor Arne, Håvard Rue, and Fred Godtliebsen. "Bayesian multiscale analysis for time series data." Computational Statistics & Data Analysis 51, no. 3 (2006): 1719–30. http://dx.doi.org/10.1016/j.csda.2006.07.034.

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10

Su, Tung-Huan, Szu-Jui Huang, Jimmy Gaspard Jean, and Chuin-Shan Chen. "Multiscale computational solid mechanics: data and machine learning." Journal of Mechanics 38 (2022): 568–85. http://dx.doi.org/10.1093/jom/ufac037.

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Abstract Multiscale computational solid mechanics concurrently connects complex material physics and macroscopic structural analysis to accelerate the application of advanced materials in the industry rather than resorting to empirical constitutive models. The rise of data-driven multiscale material modeling opens a major paradigm shift in multiscale computational solid mechanics in the era of material big data. This paper reviews state-of-the-art data-driven methods for multiscale simulation, focusing on data-driven multiscale finite element method (data-driven FE2) and data-driven multiscale

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11

Aristizábal Q, Luz Angela, and Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks." International journal of Computer Networks & Communications 13, no. 3 (2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.

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The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a

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12

Nounou, Mohamed, Hazem Nounou, Nader Meskin, and Aniruddha Datta. "Multiscale denoising of biological data: A comparative analysis." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSP27. http://dx.doi.org/10.5339/qfarf.2012.csp27.

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13

Seymour, Lynne, J. L. Starck, F. Murtagh, and A. Bijaoui. "Image Processing and Data Analysis, The Multiscale Approach." Journal of the American Statistical Association 94, no. 448 (1999): 1389. http://dx.doi.org/10.2307/2669962.

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14

Tarquis, A. M., N. R. A. Bird, A. P. Whitmore, M. C. Cartagena, and Yakov Pachepsky. "Multiscale Entropy-based Analysis of Soil Transect Data." Vadose Zone Journal 7, no. 2 (2008): 563–69. http://dx.doi.org/10.2136/vzj2007.0039.

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15

Meng Hu and Hualou Liang. "Adaptive Multiscale Entropy Analysis of Multivariate Neural Data." IEEE Transactions on Biomedical Engineering 59, no. 1 (2012): 12–15. http://dx.doi.org/10.1109/tbme.2011.2162511.

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16

FLORINDO, JOÃO BATISTA, MÁRIO DE CASTRO, and ODEMIR MARTINEZ BRUNO. "ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS." International Journal of Bifurcation and Chaos 20, no. 11 (2010): 3443–60. http://dx.doi.org/10.1142/s0218127410027805.

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This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand–Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment

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17

Nounou, M. N., H. N. Nounou, N. Meskin, A. Datta, and E. R. Dougherty. "Multiscale Denoising of Biological Data: A Comparative Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 5 (2012): 1539–45. http://dx.doi.org/10.1109/tcbb.2012.67.

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18

Jansen, Maarten. "Multiscale change point analysis in Poisson count data." Chemometrics and Intelligent Laboratory Systems 85, no. 2 (2007): 159–69. http://dx.doi.org/10.1016/j.chemolab.2006.05.014.

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19

Gogolewski, Damian. "Multiscale Data Treatment in Additive Manufacturing." Materials 16, no. 8 (2023): 3168. http://dx.doi.org/10.3390/ma16083168.

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The article assesses the impact of data treatment on the possibility of assessing the morphological features of additively manufactured spherical surfaces. Tests were carried out on specimens manufactured by PBF-LB/M additive technology, using titanium-powder-based material (Ti6Al4V). The surface topography was assessed using one of the multiscale methods—wavelet transformation. The tests carried out on a wide spectrum of mother wavelet forms emphasized the occurrence of characteristic morphological features on the surface of the tested specimens. Moreover, the significance of the impact of sp

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20

De Cola, L. "Fractal Analysis of Multiscale Spatial Autocorrelation among Point Data." Environment and Planning A: Economy and Space 23, no. 4 (1991): 545–56. http://dx.doi.org/10.1068/a230545.

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21

Murtagh, F., A. Aussem, and J. L. Starck. "Multiscale data analysis - information fusion and constant-time clustering." Vistas in Astronomy 41, no. 3 (1997): 359–64. http://dx.doi.org/10.1016/s0083-6656(97)00039-1.

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22

Ferrari, S., A. Borghese, and V. Piuri. "Multiscale models for data processing: an experimental sensitivity analysis." IEEE Transactions on Instrumentation and Measurement 50, no. 4 (2001): 995–1002. http://dx.doi.org/10.1109/19.948314.

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23

Wang, Haihong, Zhicai Luo, and Jinsheng Ning. "Multiscale edge analysis of gravity data and its applications." Geo-spatial Information Science 12, no. 3 (2009): 230–34. http://dx.doi.org/10.1007/s11806-009-0041-3.

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24

Liang, Yu, Dalei Wu, Guirong Liu, et al. "Big data-enabled multiscale serviceability analysis for aging bridges☆." Digital Communications and Networks 2, no. 3 (2016): 97–107. http://dx.doi.org/10.1016/j.dcan.2016.05.002.

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25

Fedi, Maurizio. "Global and Local Multiscale Analysis of Magnetic Susceptibility Data." Pure and Applied Geophysics 160, no. 12 (2003): 2399–417. http://dx.doi.org/10.1007/s00024-003-2401-5.

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26

Mora-Macías, Juan, JIMENEZ JACOBO AYENSA, Esther Reina-Romo, et al. "A multiscale data-driven approach for bone tissue biomechanics." Computer Methods in Applied Mechanics and Engineering 368, no. 15 (2020): 113136. https://doi.org/10.1016/j.cma.2020.113136.

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The data-driven methodology with application to continuum mechanics relies upon two main pillars: (i) experimental characterization of stress–strain pairs associated to different loading states, and (ii) numerical elaboration of the elasticity equations as an optimization (searching) algorithm using compatibility and equilibrium as constraints. The purpose of this work is to implement a multiscale data-driven approach using experimental data of cortical bone tissue at different scales. First, horse cortical bone samples are biaxially loaded and the strain fields are recorded over a regio

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27

Yang, Ying, Kun-Shan Chen, and Rui Jiang. "Modeling and Analysis of Microwave Emission from Multiscale Soil Surfaces Using AIEM Model." Remote Sensing 14, no. 22 (2022): 5899. http://dx.doi.org/10.3390/rs14225899.

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Natural rough surfaces have inherent multiscale roughness. This article presents the modeling and analysis of microwave emission from a multiscale soil surface. Unlike the linear superposition of different correlation functions with various correlation lengths, we applied the frequency modulation concept to characterize the multiscale roughness, in which the modulation does not destroy the surface’s curvature but only modifies it. The multiscale effect on emission under different observation geometries and surface parameters was examined using an AIEM model. The paper provides new insights int

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28

Wang, Xin, Xi-liang Duan, and Yang Gao. "Multiscale Asymptotic Analysis and Parallel Algorithm of Parabolic Equation in Composite Materials." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/217869.

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An efficient parallel multiscale numerical algorithm is proposed for a parabolic equation with rapidly oscillating coefficients representing heat conduction in composite material with periodic configuration. Instead of following the classical multiscale asymptotic expansion method, the Fourier transform in time is first applied to obtain a set of complex-valued elliptic problems in frequency domain. The multiscale asymptotic analysis is presented and multiscale asymptotic solutions are obtained in frequency domain which can be solved in parallel essentially without data communications. The inv

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29

Lin, Zhenhua, and Hongtu Zhu. "MFPCA: Multiscale Functional Principal Component Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4320–27. http://dx.doi.org/10.1609/aaai.v33i01.33014320.

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We consider the problem of performing dimension reduction on heteroscedastic functional data where the variance is in different scales over entire domain. The aim of this paper is to propose a novel multiscale functional principal component analysis (MFPCA) approach to address such heteroscedastic issue. The key ideas of MFPCA are to partition the whole domain into several subdomains according to the scale of variance, and then to conduct the usual functional principal component analysis (FPCA) on each individual subdomain. Both theoretically and numerically, we show that MFPCA can capture fea

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30

Lee, Gyul, Do-In Kim, Seon Kim, and Yong-June Shin. "Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis." Energies 12, no. 4 (2019): 617. http://dx.doi.org/10.3390/en12040617.

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This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multis

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31

Ling, Leevan. "Finding Numerical Derivatives for Unstructured and Noisy Data by Multiscale Kernels." SIAM Journal on Numerical Analysis 44, no. 4 (2006): 1780–800. http://dx.doi.org/10.1137/050630246.

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32

Wang, Shizhang, and Xiaoshi Qiao. "A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case." Geoscientific Model Development 15, no. 23 (2022): 8869–97. http://dx.doi.org/10.5194/gmd-15-8869-2022.

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Abstract. Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlatio

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33

Lee, Sang Heon, Adel Malallah, Akhil Datta-Gupta, and David Higdon. "Multiscale Data Integration Using Markov Random Fields." SPE Reservoir Evaluation & Engineering 5, no. 01 (2002): 68–78. http://dx.doi.org/10.2118/76905-pa.

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Summary We propose a hierarchical approach to spatial modeling based on Markov Random Fields (MRF) and multiresolution algorithms in image analysis. Unlike their geostatistical counterparts, which simultaneously specify distributions across the entire field, MRFs are based on a collection of full conditional distributions that rely on the local neighborhoods of each element. This critical focus on local specification provides several advantages:MRFs are computationally tractable and are ideally suited to simulation based computation, such as Markov Chain Monte Carlo (MCMC) methods, andmodel ex

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34

Shuangqin Cheng. "SD2S: Multiscale Granger causality analysis based on serial decomposition state space models." Journal of Electrical Systems 20, no. 7s (2024): 3546–57. http://dx.doi.org/10.52783/jes.4207.

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Granger causality, widely recognized for its ease of use, intuitive interpretability, and applicability to complex multivariate systems, facilitates the inference of causal connections between variables through observational data and elucidates their dynamic interactions. With the advancement of the significant data era, an increasing multiscale characteristic of data is evident, presenting dynamics across multiple temporal scales. Current research in this domain typically relies on vector autoregressive models and wavelet transformations, which are susceptible to noise and dependent on substa

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35

Cui, Zhanyou, Gaoli Chen, Bing Liu та Deguang Li. "A Multiscale Symbolic Dynamic Entropy Analysis of Traﬃc Flow". Journal of Advanced Transportation 2022 (30 березня 2022): 1–10. http://dx.doi.org/10.1155/2022/8389229.

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The complexity analysis of traffic flow is important for understanding the property of traffic system. Being good at analyzing the regularity and complexity, multiscale SamEn has attracted much attention and many methods have been proposed for complexity analysis of traffic flow. However, there may exist discontinuity of the calculated entropy value which makes the regularity of the traffic system difficult to understand. The phenomenon occurs due to an inappropriate selection of the parameter r in the multiscale SamEn. Moreover, it is diﬃcult to select an appropriate r for the accurate evalua

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36

Carpenter, Chris. "Advances in Special-Core-Analysis Data Interpretations of Multiscale Measurements." Journal of Petroleum Technology 65, no. 08 (2013): 94–95. http://dx.doi.org/10.2118/0813-0094-jpt.

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37

Sharma, L. N., S. Dandapat, and A. Mahanta. "Multichannel ECG Data Compression Based on Multiscale Principal Component Analysis." IEEE Transactions on Information Technology in Biomedicine 16, no. 4 (2012): 730–36. http://dx.doi.org/10.1109/titb.2012.2195322.

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38

Gyulassy, A., L. G. Nonato, P. T. Bremer, C. Silva, and V. Pascucci. "Visualization Corner: Robust Topology-Based Multiscale Analysis of Scientific Data." Computing in Science & Engineering 11, no. 5 (2009): 88–95. http://dx.doi.org/10.1109/mcse.2009.152.

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39

Sanyal, Nilotpal, and Marco A. R. Ferreira. "Bayesian hierarchical multi-subject multiscale analysis of functional MRI data." NeuroImage 63, no. 3 (2012): 1519–31. http://dx.doi.org/10.1016/j.neuroimage.2012.08.041.

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40

Li, Ta-Hsin. "Multiscale Representation and Analysis of Spherical Data by Spherical Wavelets." SIAM Journal on Scientific Computing 21, no. 3 (1999): 924–53. http://dx.doi.org/10.1137/s1064827598341463.

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41

Li, Ta-Hsin. "Multiscale wavelet analysis of scattered spherical data: design and estimation." Environmetrics 12, no. 2 (2001): 179–202. http://dx.doi.org/10.1002/1099-095x(200103)12:2<179::aid-env433>3.0.co;2-r.

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42

Deffur, Armin, Robert J. Wilkinson, Bongani M. Mayosi, and Nicola M. Mulder. "ANIMA: Association network integration for multiscale analysis." Wellcome Open Research 3 (March 12, 2018): 27. http://dx.doi.org/10.12688/wellcomeopenres.14073.1.

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Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to

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43

Deffur, Armin, Robert J. Wilkinson, Bongani M. Mayosi, and Nicola M. Mulder. "ANIMA: Association network integration for multiscale analysis." Wellcome Open Research 3 (June 5, 2018): 27. http://dx.doi.org/10.12688/wellcomeopenres.14073.2.

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Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to t

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44

Deffur, Armin, Robert J. Wilkinson, Bongani M. Mayosi, and Nicola M. Mulder. "ANIMA: Association network integration for multiscale analysis." Wellcome Open Research 3 (November 14, 2018): 27. http://dx.doi.org/10.12688/wellcomeopenres.14073.3.

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Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to t

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45

Eseholi, Tarek, François-Xavier Coudoux, Patrick Corlay, Rahmad Sadli, and Maxence Bigerelle. "A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition." Materials 13, no. 23 (2020): 5582. http://dx.doi.org/10.3390/ma13235582.

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In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing method to be decomposed into three multiscale filtered image types: Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered versions, respectively. The complete set of filtered image data constitutes the collected database. First, the images are lossless compressed using the state-of-the art High-eff

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46

Leung, Tim, and Theodore Zhao. "A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices." Mathematics 12, no. 6 (2024): 864. http://dx.doi.org/10.3390/math12060864.

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We analyze the multiscale behaviors of high-frequency intraday prices, with a focus on how asset prices are correlated over different timescales. The multiscale approach proposed in this paper is designed for the analysis of high-frequency intraday prices. It incorporates microstructure noise into the stochastic price process. We consider a noisy fractional Brownian motion model and illustrate its various statistical properties. This leads us to introduce new latent correlation and noise estimators. New numerical algorithms are developed for model estimation using empirical high-frequency data

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47

Takizawa, Kenji, Tayfun E. Tezduyar, and Takashi Kuraishi. "Multiscale space–time methods for thermo-fluid analysis of a ground vehicle and its tires." Mathematical Models and Methods in Applied Sciences 25, no. 12 (2015): 2227–55. http://dx.doi.org/10.1142/s0218202515400072.

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We present the core and special multiscale space–time (ST) methods we developed for thermo-fluid analysis of a ground vehicle and its tires. We also present application of these methods to thermo-fluid analysis of a freight truck and its rear set of tires. The core multiscale ST method is the ST variational multiscale (ST-VMS) formulation of the Navier–Stokes equations of incompressible flows with thermal coupling, which is multiscale in the way the small-scale thermo-fluid behavior is represented in the computations. The special multiscale ST method is spatially multiscale, where the thermo-f

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48

Javaherian, Mohsen, and Saeid Mollaei. "Multiscale Entropy Analysis of Gravitational Waves." Advances in High Energy Physics 2021 (March 8, 2021): 1–7. http://dx.doi.org/10.1155/2021/6643546.

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The first gravitational-wave (GW) signal was detected in the year 2015 indicating tiny distortions of spacetime caused by accelerated masses. We focused on the GW signals consisting of a peak GW strain of 1.0 × 1 0 − 21 that shows merging pairs of large masses. We applied the generalized entropy known as multiscale entropy to the GW interval time series recorded by different observatories (H1, L1, and V1). This enables us to investigate the behavior of entropies on different scales as a method of studying complexity and organization. We found that the entropies of GW interval data with similar

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49

Shou, Zhaoyu, Pan Chen, Hui Wen, Jinghua Liu, and Huibing Zhang. "MOOC Dropout Prediction Based on Multidimensional Time-Series Data." Mathematical Problems in Engineering 2022 (April 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/2213292.

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Massive open online courses have attracted millions of learners worldwide with flexible learning options. However, online learning differs from offline education in that the lack of communicative feedback is a drawback that magnifies high dropout rates. The analysis and prediction of student’s online learning process can help teachers find the students with dropout tendencies in time and provide additional help. Previous studies have shown that analyzing learning behaviors at different time scales leads to different prediction results. In addition, noise in the time-series data of student beha

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50

GRANELL, CLARA, SERGIO GÓMEZ, and ALEX ARENAS. "UNSUPERVISED CLUSTERING ANALYSIS: A MULTISCALE COMPLEX NETWORKS APPROACH." International Journal of Bifurcation and Chaos 22, no. 07 (2012): 1230023. http://dx.doi.org/10.1142/s0218127412300236.

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Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a standard benchmark of data clustering and compare their performance.

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