Relevant bibliographies by topics / Wavelet-multifractal

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Wavelet-multifractal'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Wavelet-multifractal"

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Barral, Julien, and Stéphane Seuret. "From Multifractal Measures to Multifractal Wavelet Series." Journal of Fourier Analysis and Applications 11, no. 5 (2005): 589–614. http://dx.doi.org/10.1007/s00041-005-5006-9.

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JIANG, ZHI-QIANG, XING-LU GAO, WEI-XING ZHOU, and H. EUGENE STANLEY. "MULTIFRACTAL CROSS WAVELET ANALYSIS." Fractals 25, no. 06 (2017): 1750054. http://dx.doi.org/10.1142/s0218348x17500542.

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Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For
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Arneodo, Alain, Benjamin Audit, Pierre Kestener, and Stephane Roux. "Wavelet-based multifractal analysis." Scholarpedia 3, no. 3 (2008): 4103. http://dx.doi.org/10.4249/scholarpedia.4103.

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FIGLIOLA, ALEJANDRA, EDUARDO SERRANO, GUSTAVO PACCOSI, and MARIEL ROSENBLATT. "ABOUT THE EFFECTIVENESS OF DIFFERENT METHODS FOR THE ESTIMATION OF THE MULTIFRACTAL SPECTRUM OF NATURAL SERIES." International Journal of Bifurcation and Chaos 20, no. 02 (2010): 331–39. http://dx.doi.org/10.1142/s0218127410025788.

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Complex natural systems present characteristics of scalar invariance. This behavior has been experimentally verified and a large related bibliography has been reported. Multifractal Formalism is a way to evaluate this kind of behavior. In the past years, different numerical methods to estimate the multifractal spectrum have been proposed. These methods could be classified into those that originated from the wavelet analysis and others from numerical approximations like the Multifractal Detrended Fluctuation Analysis (MFDFA), proposed by Kantelhardt and Stanley. Recently, S. Jaffard and co-work
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MURGUÍA, J. S., M. MEJÍA CARLOS, C. VARGAS-OLMOS, M. T. RAMÍREZ-TORRES, and H. C. ROSU. "WAVELET MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS OF ENCRYPTION AND DECRYPTION MATRICES." International Journal of Modern Physics C 24, no. 09 (2013): 1350069. http://dx.doi.org/10.1142/s0129183113500691.

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In this paper, we study in detail the multifractal features of the main matrices of an encryption system based on a rule-90 cellular automaton. For this purpose, we consider the scaling method known as the wavelet transform multifractal detrended fluctuation analysis (WT-MFDFA). In addition, we analyze the multifractal structure of the matrices of different dimensions, and find that there are minimal differences in all the examined multifractal quantities such as the multifractal support, the most frequent singularity exponent, and the generalized Hurst exponent.
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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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Oral, Emrah, and Gazanfer Unal. "Modeling and forecasting multifractal wavelet scale: Western market versus eastern market." International Journal of Modern Physics C 29, no. 11 (2018): 1850109. http://dx.doi.org/10.1142/s0129183118501097.

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This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out o
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Ibrahim Mahmoud, Masharif Mahmoud, Anouar Ben Mabrouk, and Mohsin Hassan Abdallah Hashim. "Wavelet multifractal modeling and prediction of transmembrane proteins series." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 06 (2016): 1650044. http://dx.doi.org/10.1142/s0219691316500442.

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We propose in this paper to study the multifractal properties of a special case of biological series, such as the transmembrane proteins. We prove that such series has a multifractal structure allowing their modeling by means of multifractal models issued from wavelet bases to be efficient. We apply the developed method to a numerical example in order to show its efficiency.
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Chen, Chang Zheng, Yu Zhang, Quan Gu, and Yan Ling Gu. "Wind Turbine Generator Fault Detection by Wavelet-Based Multifractal Analysis." Advanced Materials Research 644 (January 2013): 346–49. http://dx.doi.org/10.4028/www.scientific.net/amr.644.346.

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It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the com
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AOUIDI, JAMIL, and ANOUAR BEN MABROUK. "A WAVELET MULTIFRACTAL FORMALISM FOR SIMULTANEOUS SINGULARITIES OF FUNCTIONS." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 01 (2013): 1450009. http://dx.doi.org/10.1142/s021969131450009x.

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In this paper, a wavelet multifractal analysis is developed which permits to characterize simultaneous singularities for a vector of functions. An associated multifractal formalism is introduced and checked for the case of functions involving self similar aspects.
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Dissertations / Theses on the topic "Wavelet-multifractal"

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Wirsing, Karlton E. "Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98781.

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The sun is constantly emitting electrons and ions as magnetized plasma, forming the solar wind and carrying with it the sun’s rotating magnetic field. The solar wind subsequently interacts with Earth’s magnetosphere and the magnetospheres of other planets. Occasionally large amounts of magnetized plasma are released at one time in a Corona mass ejection (CME). When the CME interacts with Earth’s magnetic field, it induces perturbations that may have a significant impact on critical infrastructure, for instance, by disturbing communication systems, and inducing currents on pipelines and electri
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Jorge, Lúcio André de Castro. "Metodologia de fusão de vídeos e sons para monitoração de comportamento de insetos." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-19102011-110608/.

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Este trabalho apresenta uma nova abordagem para fusão de vídeo e som diretamente no espaço de atributos visando otimizar a identificação do comportamento de insetos. Foi utilizado o detector de Harris para rastreamento dos insetos, assim como a técnica inovadora Wavelet-Multifractal para análise de som. No caso da Wavelet-Multifractal, foram testadas várias Wavelet-mães, sendo a Morlet a melhor escolha para sons de insetos. Foi proposto a Wavelet Módulo Máximo para extrair atributos multifractais dos sons para serem utilizados no reconhecimento de padrões de comportamento de insetos. A abordag
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Li, Junhua 1970. "Scale analysis in remote sensing based on wavelet transform and multifractal modeling." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82916.

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With the development of Geographical Information System (GIS) and remote sensing techniques, a great deal of data has provided a set of continuous samples of the earth surface from local, regional to global scales. Several multi-scale, multi-resolution, pyramid or hierarchical methods and statistical methods have been developed and used to investigate the scaling property of remotely sensed data: local variance, texture method, scale variance, semivariogram, and fractal analysis. This research introduces the wavelet transform into the realm of scale study in remote sensing and answers t
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Dahale, Radhika. "Optoelectronic Multifractal Wavelet Analysis for Fast and Accurate Detection of Rainfall in Weather Radar Images." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/97.

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In this thesis we propose an automated process for the removal of non-precipitation echoes present in weather radar signals and accurate detection of rainfall. The process employs multifractal analysis using directional Gabor wavelets for accurate detection of the rain events. An optoelectronic joint transform correlator is proposed to provide ultra fast processing and wavelet analysis. Computer simulations of the proposed system show that the proposed algorithm is successful in the detecting rainfall accurately in radar images. The accuracy of the algorithms proposed are compared to
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Gonçalves, Bruno Henrique Pereira. "Alocação dinâmica de recursos em redes OFDM multiusuários baseada em banda efetiva." Universidade Federal de Goiás, 2013. http://repositorio.bc.ufg.br/tede/handle/tede/3563.

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Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-11-06T17:22:37Z No. of bitstreams: 2 Dissertacao - Bruno Henrique Pereira Gonçalves - 2013.pdf: 2765842 bytes, checksum: 73031d592b28fd2dc03d6bbdfa04cc34 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)<br>Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2014-11-06T17:22:46Z (GMT) No. of bitstreams: 2 Dissertacao - Bruno Henrique Pereira Gonçalves - 2013.pdf: 2765842 bytes, checksum: 73031d592b28fd2dc03d6bbdfa04cc34 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac9571
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Combrexelle, Sébastien. "Multifractal analysis for multivariate data with application to remote sensing." Phd thesis, Toulouse, INPT, 2016. http://oatao.univ-toulouse.fr/16477/1/Combrexelle.pdf.

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Texture characterization is a central element in many image processing applications. Texture analysis can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the coefficients or wavelet leaders. Although successfully applied in various contexts, multi fractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small sample sizes. Second, multifr
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Ångman, Mikael, and Hampus Viken. "Automatic segmentation of articular cartilage in arthroscopic images using deep neural networks and multifractal analysis." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166035.

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Osteoarthritis is a large problem affecting many patients globally, and diagnosis of osteoarthritis is often done using evidence from arthroscopic surgeries. Making a correct diagnosis is hard, and takes years of experience and training on thousands of images. Therefore, developing an automatic solution to perform the diagnosis would be extremely helpful to the medical field. Since machine learning has been proven to be useful and effective at classifying and segmenting medical images, this thesis aimed at solving the problem using machine learning methods. Multifractal analysis has also been
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ZHU, XIANGDONG. "WAVELET-BASED SIGNAL ANALYSIS FOR THE ENVIRONMENTAL HEALTH RESEARCH." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085064472.

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Wendt, Herwig. "Contributions of Wavelet Leaders and Bootstrap to Multifractal Analysis : Images, estimation performance, dependence structure and vanishing moments. Confidence Intervals and Hypothesis Tests." Lyon, École normale supérieure (sciences), 2008. http://www.theses.fr/2008ENSL0474.

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Cette thèse étudie l'apport à l'analyse multifractale de l'utilisation des coefficients d'ondelettes dominants et des techniques statistiques bootstrap. Les propriétés statistiques de procédures d'analyse multifractale construites à partir de coefficients dominants sont caractérisées. L'extension aux images est validée. Plusieurs difficultés théoriques, cruciales en pratique, sont étudiées : régularité minimale, espaces fonctionnels, effet de linéarisation. L'originalité de notre approche bootstrap réside dans la construction de blocs temps-échelle, qui permet le calcul d'intervalles de confia
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Siddiqui, Sharjeel. "Wavelet and IFS compression of multifractal object boundaries." 2005. http://hdl.handle.net/1993/20209.

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Book chapters on the topic "Wavelet-multifractal"

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Ouahabi, Abdeldjalil. "Discrete Wavelet Transform-Based Multifractal Analysis." In Signal and Image Multiresolution Analysis. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118568767.ch2.

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Aouit, Djedjiga Ait, and Abdeldjalil Ouahabi. "Wavelet-Based Multifractal Identification of Fracture Stages." In Damage and Fracture Mechanics. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2669-9_54.

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Jaffard, S. "Wavelet expansions, function spaces and multifractal analysis." In Twentieth Century Harmonic Analysis — A Celebration. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0662-0_6.

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Nicolis, Orietta. "WAVELET-BASED MULTIFRACTAL ANALYSIS OF LANDSAT 8 IMAGES." In Oil and Gas Exploration. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119227519.ch2.

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Riedi, Rudolf H., Vinay J. Ribeiro, Matthew S. Crouse, and Richard G. Baraniuk. "Network Traffic Modeling Using a Multifractal Wavelet Model." In European Congress of Mathematics. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8266-8_54.

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Banerjee, Santo, D. Easwaramoorthy, and A. Gowrisankar. "Multifractal Analysis and Wavelet Decomposition in EEG Signal Classification." In Understanding Complex Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62672-3_5.

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Lawrence, J. K., A. C. Cadavid, and A. A. Ruzmaikin. "Wavelet and Multifractal Analyses of Spatial and Temporal Solar Activity Variations." In Statistical Challenges in Modern Astronomy II. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1968-2_35.

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Karaca, Yeliz. "Wavelet-based Multifractal Spectrum Estimation in Hepatitis Virus Classification Models by Using Artificial Neural Network Approach." In Global Virology III: Virology in the 21st Century. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29022-1_4.

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Arfaoui, Sabrine, Anouar Ben Mabrouk, and Carlo Cattani. "Wavelets for fractal and multifractal functions." In Wavelet Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003096924-8.

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Gerasimova, Evgeniya, Benjamin Audit, Stephane-G. Roux, et al. "A Wavelet-Based Method for Multifractal Analysis of Medical Signals: Application to Dynamic Infrared Thermograms of Breast Cancer." In Nonlinear Dynamics of Electronic Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08672-9_34.

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Conference papers on the topic "Wavelet-multifractal"

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Jaffard, Stephane. "Wavelet analysis of multifractal functions." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1995. http://dx.doi.org/10.1117/12.217584.

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Ouahabi, Abdeldjalil, Stephane Jaffard, and Djedjiga Ait Aouit. "Wavelet based Multifractal Analysis in Fractography." In 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2008. http://dx.doi.org/10.1109/ipta.2008.4743742.

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Uchaev, Dm V., D. V. Uchaev, and V. A. Malinnikov. "Orthogonal wavelet moments and their multifractal invariants." In Seventh International Conference on Machine Vision (ICMV 2014), edited by Antanas Verikas, Branislav Vuksanovic, Petia Radeva, and Jianhong Zhou. SPIE, 2015. http://dx.doi.org/10.1117/12.2180706.

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Wendt, Herwig, Patrice Abry, Stephane Jaffard, Hui Ji, and Zuowei Shen. "Wavelet Leader multifractal analysis for texture classification." In 2009 16th IEEE International Conference on Image Processing (ICIP 2009). IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414273.

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Dick, Olga E., and Irina A. Mochovikova. "Multifractal and wavelet analysis of epileptic seizures." In Selected Papers from the 3rd Chaotic Modeling and Simulation International Conference (CHAOS2010). WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814350341_0018.

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Gao, Jianbo, Yi Zheng, and Jing Hu. "Long-Range Temporal Correlations, Multifractality, and the Causal Relation Between Neural Inputs and Movements." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6081.

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Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal correlations. We find that MF-DFA and wavelet multifractal
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Jaffard, Stephane, Stephane Seuret, Herwig Wendt, Patrice Abry, and Roberto Leonarduzzi. "A multivariate multifractal analysis of lacunary wavelet series." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022481.

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Roux, S. G., P. Abry, B. Vedel, S. Jaffard, and H. Wendt. "Hyperbolic wavelet leaders for anisotropic multifractal texture analysis." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533022.

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Jagtap, Jaidip, Sayantan Ghosh, Prasanta K. Panigrahi, and Asima Pradhan. "Wavelet-based multifractal analysis of laser biopsy imagery." In SPIE BiOS, edited by Valery V. Tuchin, Donald D. Duncan, Kirill V. Larin, Martin J. Leahy, and Ruikang K. Wang. SPIE, 2012. http://dx.doi.org/10.1117/12.907330.

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Seghouane, Abd-Krim. "An adaptive Bayesian wavelet thresholding approach to Multifractal signal denoising." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835226.

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