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Journal articles on the topic 'Multiresolution analysis'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

San Antolín, Angel. "On translation invariant multiresolution analysis." Glasnik Matematicki 49, no. 2 (December 18, 2014): 377–94. http://dx.doi.org/10.3336/gm.49.2.11.

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2

Lim, Jae Kun. "Gramian analysis of multivariate frame multiresolution analyses." Bulletin of the Australian Mathematical Society 66, no. 2 (October 2002): 291–300. http://dx.doi.org/10.1017/s0004972700040132.

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We perform a Gramian analysis of a frame multiresolution analysis to give a condition for it to admit a minimal wavelet set and to show that the frame bounds of the natural generator for the wavelet space of a degenerate frame multiresolution analysis shrink.
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3

Yanjun Zhao and S. Belkasim. "Multiresolution Fourier Descriptors for Multiresolution Shape Analysis." IEEE Signal Processing Letters 19, no. 10 (October 2012): 692–95. http://dx.doi.org/10.1109/lsp.2012.2210040.

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4

Combettes, P. L., and J. C. Pesquet. "Convex multiresolution analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 12 (1998): 1308–18. http://dx.doi.org/10.1109/34.735804.

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5

Bhosale, Bharat. "Curvelet Based Multiresolution Analysis of Graph Neural Networks." International Journal of Applied Physics and Mathematics 4, no. 5 (2014): 313–23. http://dx.doi.org/10.7763/ijapm.2014.v4.304.

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6

Rakić, Dušan. "Multiresolution expansion in." Integral Transforms and Special Functions 20, no. 3-4 (April 2009): 231–38. http://dx.doi.org/10.1080/10652460802568143.

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7

Massopust, Peter R. "Generalized Multiresolution Schemes." Journal of Mathematical Analysis and Applications 221, no. 2 (May 1998): 574–94. http://dx.doi.org/10.1006/jmaa.1998.5917.

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8

Moore, John A., Ying Li, Devin T. O'Connor, Wylie Stroberg, and Wing Kam Liu. "Advancements in multiresolution analysis." International Journal for Numerical Methods in Engineering 102, no. 3-4 (January 13, 2015): 784–807. http://dx.doi.org/10.1002/nme.4840.

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9

Konik, Hubert, Alain Tremeau, and Bernard Laget. "Multiresolution Color Image Analysis." Color and Imaging Conference 3, no. 1 (January 1, 1995): 82–85. http://dx.doi.org/10.2352/cic.1995.3.1.art00021.

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10

Mickelin, Oscar, and Sertac Karaman. "Multiresolution Low-rank Tensor Formats." SIAM Journal on Matrix Analysis and Applications 41, no. 3 (January 2020): 1086–114. http://dx.doi.org/10.1137/19m1284579.

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11

Baggett, Lawrence W., Veronika Furst, Kathy D. Merrill, and Judith A. Packer. "Classification of generalized multiresolution analyses." Journal of Functional Analysis 258, no. 12 (June 2010): 4210–28. http://dx.doi.org/10.1016/j.jfa.2009.12.001.

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12

Zhang, Liyan, Jian Ma, Bin Ran, and Lixin Yan. "Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy." Modelling and Simulation in Engineering 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/3694791.

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The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.
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13

Jia, R. Q. "Multiresolution of Lp Spaces." Journal of Mathematical Analysis and Applications 184, no. 3 (June 1994): 620–39. http://dx.doi.org/10.1006/jmaa.1994.1226.

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14

Sharma, Vikram, and P. Manchanda. "WAVELET PACKETS ASSOCIATED WITH NONUNIFORM MULTIRESOLUTION ANALYSIS ON POSITIVE HALF LINE." Asian-European Journal of Mathematics 06, no. 01 (March 2013): 1350007. http://dx.doi.org/10.1142/s1793557113500071.

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Gabardo and Nashed [Nonuniform multiresolution analysis and spectral pairs, J. Funct. Anal.158 (1998) 209–241] introduced the Nonuniform multiresolution analysis (NUMRA) whose translation set is not a group. Farkov [Orthogonal p-wavelets on ℝ+, in Proc. Int. Conf. Wavelets and Splines (St. Petersburg State University, St. Petersburg, 2005), pp. 4–26] studied multiresolution analysis (MRA) on positive half line and constructed associated wavelets. Meenakshi et al. [Wavelets associated with Nonuniform multiresolution analysis on positive half line, Int. J. Wavelets, Multiresolut. Inf. Process.10(2) (2011) 1250018, 27pp.] studied NUMRA on positive half line and proved the analogue of Cohen's condition for the NUMRA on positive half line. We construct the associated wavelet packets for such an MRA and study its properties.
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15

Kapoor, G. P., and Srijanani Anurag Prasad. "Multiresolution Analysis Based on Coalescence Hidden-Variable Fractal Interpolation Functions." International Journal of Computational Mathematics 2014 (December 11, 2014): 1–7. http://dx.doi.org/10.1155/2014/531562.

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Multiresolution analysis arising from Coalescence Hidden-variable Fractal Interpolation Functions (CHFIFs) is developed. The availability of a larger set of free variables and constrained variables with CHFIF in multiresolution analysis based on CHFIFs provides more control in reconstruction of functions in L2(R) than that provided by multiresolution analysis based only on Affine Fractal Interpolation Functions (AFIFs). Our approach consists of introduction of the vector space of CHFIFs, determination of its dimension and construction of Riesz bases of vector subspaces Vk, k∈Z, consisting of certain CHFIFs in L2(R)∩C0(R).
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16

Borowska, Marta, and Natalia Białobłocka. "Multiresolution Analysis of EEG Signals." Studies in Logic, Grammar and Rhetoric 47, no. 1 (December 1, 2016): 21–31. http://dx.doi.org/10.1515/slgr-2016-0044.

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Abstract This paper reports on a multiresolution analysis of EEG signals. The dominant frequency components of signals with and without observed epileptic discharges were compared. The study showed that there were significant differences in dominant frequency between the signals with epileptic discharges and the signals without discharges. This gives the ability to identify epilepsy during EEG examination. The frequency of the signals coming from the frontal, central, parietal and occipital channels are similar. Multiresolution analysis can be used to describe the activity of brain waves and to try to predict epileptic seizures, thereby contributing to precise medical diagnoses.
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17

Abgrall, Rémi. "Multiresolution Representation in Unstructured Meshes." SIAM Journal on Numerical Analysis 35, no. 6 (December 1998): 2128–46. http://dx.doi.org/10.1137/s0036142997315056.

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18

Dai, X., Y. Diao, Q. Gu, and D. Han. "Wavelets with Frame Multiresolution Analysis." Journal of Fourier Analysis and Applications 9, no. 1 (January 1, 2003): 39–48. http://dx.doi.org/10.1007/s00041-003-0001-5.

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19

Wojdyłło, Piotr. "Wavelets and Mallat's Multiresolution Analysis." Fundamenta Informaticae 34, no. 4 (1998): 469–74. http://dx.doi.org/10.3233/fi-1998-34409.

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20

Chuang He and J. M. F. Moura. "Focused detection via multiresolution analysis." IEEE Transactions on Signal Processing 46, no. 4 (April 1998): 1094–104. http://dx.doi.org/10.1109/78.668559.

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21

Dahlke, Stephan, Wolfgang Dahmen, Ilona Weinreich, and Eberhard Schmitt. "Multiresolution analysis and wavelets onS2andS3." Numerical Functional Analysis and Optimization 16, no. 1-2 (January 1995): 19–41. http://dx.doi.org/10.1080/01630569508816605.

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22

Biyikli, Emre, and Albert C. To. "Multiresolution molecular mechanics: Adaptive analysis." Computer Methods in Applied Mechanics and Engineering 305 (June 2016): 682–702. http://dx.doi.org/10.1016/j.cma.2016.02.038.

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23

Telesca, Luciano, Vincenzo Lapenna, and Nikos Alexis. "Multiresolution wavelet analysis of earthquakes." Chaos, Solitons & Fractals 22, no. 3 (November 2004): 741–48. http://dx.doi.org/10.1016/j.chaos.2004.02.021.

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24

Román-Roldán, Ramón, JoséJ Quesada-Molina, and José Martínez-Aroza. "Multiresolution-information analysis for images." Signal Processing 24, no. 1 (July 1991): 77–91. http://dx.doi.org/10.1016/0165-1684(91)90085-w.

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25

Su, M. S., W. L. Hwang, and K. Y. Cheng. "Analysis on Multiresolution Mosaic Images." IEEE Transactions on Image Processing 13, no. 7 (July 2004): 952–59. http://dx.doi.org/10.1109/tip.2004.828416.

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26

Sturani, R., and R. Terenzi. "Adaptive multiresolution for wavelet analysis." Journal of Physics: Conference Series 122 (July 1, 2008): 012036. http://dx.doi.org/10.1088/1742-6596/122/1/012036.

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27

Beam, Richard M., and Robert F. Warming. "Multiresolution Analysis and Supercompact Multiwavelets." SIAM Journal on Scientific Computing 22, no. 4 (January 2000): 1238–68. http://dx.doi.org/10.1137/s1064827596311906.

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28

De Michele, L., and P. M. Soardi. "On multiresolution analysis of multiplicityd." Monatshefte für Mathematik 124, no. 3 (September 1997): 255–72. http://dx.doi.org/10.1007/bf01298247.

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29

Jiang, Huikun, Dengfeng Li, and Ning Jin. "Multiresolution analysis on local fields." Journal of Mathematical Analysis and Applications 294, no. 2 (June 2004): 523–32. http://dx.doi.org/10.1016/j.jmaa.2004.02.026.

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30

McVeigh, Cahal, Franck Vernerey, Wing Kam Liu, and L. Cate Brinson. "Multiresolution analysis for material design." Computer Methods in Applied Mechanics and Engineering 195, no. 37-40 (July 2006): 5053–76. http://dx.doi.org/10.1016/j.cma.2005.07.027.

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31

Harizanov, Stanislav. "Stability of nonlinear multiresolution analysis." PAMM 8, no. 1 (December 2008): 10933–34. http://dx.doi.org/10.1002/pamm.200810933.

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32

Gabardo, Jean-Pierre, and M. Zuhair Nashed. "Nonuniform Multiresolution Analyses and Spectral Pairs." Journal of Functional Analysis 158, no. 1 (September 1998): 209–41. http://dx.doi.org/10.1006/jfan.1998.3253.

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33

MEENAKSHI, P. MANCHANDA, and A. H. SIDDIQI. "WAVELETS ASSOCIATED WITH NONUNIFORM MULTIRESOLUTION ANALYSIS ON POSITIVE HALF-LINE." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 02 (March 2012): 1250018. http://dx.doi.org/10.1142/s021969131250018x.

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Gabardo and Nashed have studied nonuniform multiresolution analysis based on the theory of spectral pairs in a series of papers, see Refs. 4 and 5. Farkov,3 has extended the notion of multiresolution analysis on locally compact Abelian groups and constructed the compactly supported orthogonal p-wavelets on L2(ℝ+). We have considered the nonuniform multiresolution analysis on positive half-line. The associated subspace V0 of L2(ℝ+) has an orthonormal basis, a collection of translates of the scaling function φ of the form {φ(x ⊖ λ)}λ∈Λ+ where Λ+ = {0, r/N} + ℤ+, N > 1 (an integer) and r is an odd integer with 1 ≤ r ≤ 2N - 1 such that r and N are relatively prime and ℤ+ is the set of non-negative integers. We find the necessary and sufficient condition for the existence of associated wavelets and derive the analogue of Cohen's condition for the nonuniform multiresolution analysis on the positive half-line.
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34

Shah, Firdous A. "Frame Multiresolution Analysis on Local Fields of Positive Characteristic." Journal of Operators 2015 (February 16, 2015): 1–8. http://dx.doi.org/10.1155/2015/216060.

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We present a notion of frame multiresolution analysis on local fields of positive characteristic based on the theory of shift-invariant spaces. In contrast to the standard setting, the associated subspace V0 of L2(K) has a frame, a collection of translates of the scaling function φ of the form φ(·-u(k)):k∈N0, where N0 is the set of nonnegative integers. We investigate certain properties of multiresolution subspaces which provides the quantitative criteria for the construction of frame multiresolution analysis (FMRA) on local fields of positive characteristic. Finally, we provide a characterization of wavelet frames associated with FMRA on local field K of positive characteristic using the shift-invariant space theory.
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35

Kim, Hong Oh, Rae Young Kim, and Jae Kun Lim. "Local analysis of frame multiresolution analysis with a general dilation matrix." Bulletin of the Australian Mathematical Society 67, no. 2 (April 2003): 285–95. http://dx.doi.org/10.1017/s000497270003375x.

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A multivariate semi-orthogonal frame multiresolution analysis with a general integer dilation matrix and multiple scaling functions is considered. We first derive the formulas of the lengths of the inital (central) shift-invariant space V0 and the next dilation space V1, and, using these formulas, we then address the problem of the number of the elements of a wavelet set, that is, the length of the shift-invariant space W0 := V1 ⊖ V0. Finally, we show that there does not exist a ‘genuine’ frame multiresolution analysis for which V0 and V1 are quasi-stable spaces satisfying the usual length condition.
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36

Alfarraj, Motaz, Yazeed Alaudah, Zhiling Long, and Ghassan AlRegib. "Multiresolution analysis and learning for computational seismic interpretation." Leading Edge 37, no. 6 (June 2018): 443–50. http://dx.doi.org/10.1190/tle37060443.1.

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We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrated seismic data. Namely, we explore the Gaussian pyramid, the discrete wavelet transform, Gabor filters, and the curvelet transform. These techniques are examined in a seismic structure labeling case study on the Netherlands offshore F3 block. In seismic structure labeling, a seismic volume is automatically segmented and classified according to the underlying subsurface structure using texture attributes. Our results show that multiresolution attributes improve the labeling performance compared to using seismic amplitude alone. Moreover, directional multiresolution attributes, such as the curvelet transform, are more effective than the nondirectional attributes in distinguishing different subsurface structures in large seismic data sets and can greatly help the interpretation process.
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37

Farkov, Yury. "Multiresolution analysis and wavelets on Vilenkin groups." Facta universitatis - series: Electronics and Energetics 21, no. 3 (2008): 309–25. http://dx.doi.org/10.2298/fuee0803309f.

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This paper gives a review of multiresolution analysis and compactly sup- ported orthogonal wavelets on Vilenkin groups. The Strang-Fix condition, the partition of unity property, the linear independence, the stability, and the orthonormality of 'integer shifts' of the corresponding refinable functions are considered. Necessary and sufficient conditions are given for refinable functions to generate a multiresolution analysis in the L2-spaces on Vilenkin groups. Several examples are provided to illustrate these results. .
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38

Zhu, Yuan, Wenjun Gao, and Dengfeng Li. "Characterization of Generators for Multiresolution Analyses with Composite Dilations." Abstract and Applied Analysis 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/850850.

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This paper introduces multiresolution analyses with composite dilations (AB-MRAs) and addresses frame multiresolution analyses with composite dilations in the setting of reducing subspaces ofL2(ℝn)(AB-RMRAs). We prove that an AB-MRA can induce an AB-RMRA on a given reducing subspaceL2(S)∨. For a general expansive matrix, we obtain the characterizations for a scaling function to generate an AB-RMRA, and the main theorems generalize the classical results. Finally, some examples are provided to illustrate the general theory.
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39

Aràndiga, Francesc, and Vicente F. Candela. "Multiresolution Standard Form of a Matrix." SIAM Journal on Numerical Analysis 33, no. 2 (April 1996): 417–34. http://dx.doi.org/10.1137/0733022.

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40

Getreuer, Pascal, and François G. Meyer. "ENO Multiresolution Schemes with General Discretizations." SIAM Journal on Numerical Analysis 46, no. 6 (January 2008): 2953–77. http://dx.doi.org/10.1137/060663763.

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41

Tan, Xiaojing, Ming Zou, and Xiqin He. "Target Recognition in SAR Images Based on Multiresolution Representations with 2D Canonical Correlation Analysis." Scientific Programming 2020 (February 24, 2020): 1–9. http://dx.doi.org/10.1155/2020/7380790.

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This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.
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42

Cohen, Albert, and Nira Dyn. "Nonstationary Subdivision Schemes and Multiresolution Analysis." SIAM Journal on Mathematical Analysis 27, no. 6 (November 1996): 1745–69. http://dx.doi.org/10.1137/s003614109427429x.

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43

Freeden, Willi, and Michael Schreiner. "Multiresolution Analysis by Spherical Up Functions." Constructive Approximation 23, no. 3 (December 21, 2005): 241–59. http://dx.doi.org/10.1007/s00365-005-0613-x.

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44

Hardin, Douglas P., Bruce Kessler, and Peter R. Massopust. "Multiresolution analyses based on fractal functions." Journal of Approximation Theory 71, no. 1 (October 1992): 104–20. http://dx.doi.org/10.1016/0021-9045(92)90134-a.

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45

Beśka, Marek, and Karol Dziedziul. "Multiresolution and Approximation and Hardy Spaces." Journal of Approximation Theory 88, no. 2 (February 1997): 154–67. http://dx.doi.org/10.1006/jath.1996.3019.

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46

Chi, Taishih, Powen Ru, and Shihab A. Shamma. "Multiresolution spectrotemporal analysis of complex sounds." Journal of the Acoustical Society of America 118, no. 2 (August 2005): 887–906. http://dx.doi.org/10.1121/1.1945807.

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47

Caso, Gregory, and C. C. Jay Kuo. "Multiresolution Analysis of Fractal Image Compression." Fractals 05, supp01 (April 1997): 215–29. http://dx.doi.org/10.1142/s0218348x97000772.

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In this research, we perform a multiresolution analysis of the mappings used in fractal image compression. We derive the transform-domain structure of the mappings and demonstrate a close connection between fractal image compression and wavelet transform coding using the Haar basis. We show that under certain conditions, the mappings correspond to a hierarchy of affine mappings between the subbands of the transformed image. Our analysis provides new insights into the mechanism underlying fractal image compression, leads to a new non-iterative transform-domain decoding algorithm, and suggests a new transform-domain encoding method with extensions to wavelets other than the Haar transform.
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48

Thakor, N. V., Guo Xin-Rong, Sun Yi-Chun, and D. F. Hanley. "Multiresolution wavelet analysis of evoked potentials." IEEE Transactions on Biomedical Engineering 40, no. 11 (1993): 1085–94. http://dx.doi.org/10.1109/10.245625.

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49

Lukomskii, S. F. "Riesz multiresolution analysis on Vilenkin groups." Doklady Mathematics 90, no. 1 (August 2014): 412–15. http://dx.doi.org/10.1134/s1064562414040061.

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50

Miller, Paul. "Multiresolution correlator analysis and filter design." Applied Optics 35, no. 29 (October 10, 1996): 5790. http://dx.doi.org/10.1364/ao.35.005790.

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