Relevant bibliographies by topics / Wavelets (Mathematics) – Data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wavelets (Mathematics) – Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Wavelets (Mathematics) – Data processing"

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Ahmadi, H., G. Dumont, F. Sassani, and R. Tafreshi. "Performance of Informative Wavelets for Classification and Diagnosis of Machine Faults." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 03 (2003): 275–89. http://dx.doi.org/10.1142/s0219691303000189.

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This paper deals with an application of wavelets for feature extraction and classification of machine faults in a real-world machine data analysis environment. We have utilized informative wavelet algorithm to generate wavelets and subsequent coefficients that are used as feature variables for classification and diagnosis of machine faults. Informative wavelets are classes of functions generated from a given analyzing wavelet in a wavelet packet decomposition structure in which for the selection of best wavelets, concepts from information theory, i.e. mutual information and entropy are utilize
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AMINGHAFARI, MINA, and JEAN-MICHEL POGGI. "FORECASTING TIME SERIES USING WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 05 (2007): 709–24. http://dx.doi.org/10.1142/s0219691307002002.

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This paper deals with wavelets in time series, focusing on statistical forecasting purposes. Recent approaches involve wavelet decompositions in order to handle non-stationary time series in such context. A method, proposed by Renaud et al.,11 estimates directly the prediction equation by direct regression of the process on the Haar non-decimated wavelet coefficients depending on its past values. In this paper, this method is studied and extended in various directions. The new variants are used first for stationary data and after for stationary data contaminated by a deterministic trend.
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TANAKA, NOBUATSU. "A SIMPLE BUT EFFICIENT PRECONDITIONING FOR CONJUGATE GRADIENT POISSON SOLVER USING HAAR WAVELET." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 02 (2006): 273–84. http://dx.doi.org/10.1142/s0219691306001233.

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This paper describes a wavelet-based preconditioning technique for conjugate gradient method for linear systems derived from the Poisson equation. The linear systems solved with a conventional iterative matrix solver resulted in a marked increase in computing time with respect to an increase in grid points. Use of our wavelet-based technique leads to a matrix with a bounded condition number so that computing time is reduced significantly. In this study, one of the simplest wavelets, the Haar wavelet, is used for the purpose of developing a simple but efficient preconditioning algorithm. Simple
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ČASTOVÁ, NINA, DAVID HORÁK, and ZDENĚK KALÁB. "DESCRIPTION OF SEISMIC EVENTS USING WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 03 (2006): 405–14. http://dx.doi.org/10.1142/s0219691306001336.

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This paper deals with engineering application of wavelet transform for processing of real seismological signals. Methodology for processing of these slight signals using wavelet transform is presented in this paper. Briefly, three basic aims are connected with this procedure:. 1. Selection of optimal wavelet and optimal wavelet basis B opt for selected data set based on minimal entropy: B opt = arg min B E(X,B). The best results were reached by symmetric complex wavelets with scaling coefficients SCD-6. 2. Wavelet packet decomposition and filtration of data using universal criterion of thresho
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DeVore, Ronald A., and Bradley J. Lucier. "Wavelets." Acta Numerica 1 (January 1992): 1–56. http://dx.doi.org/10.1017/s0962492900002233.

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The subject of ‘wavelets’ is expanding at such a tremendous rate that it is impossible to give, within these few pages, a complete introduction to all aspects of its theory. We hope, however, to allow the reader to become sufficiently acquainted with the subject to understand, in part, the enthusiasm of its proponents toward its potential application to various numerical problems. Furthermore, we hope that our exposition can guide the reader who wishes to make more serious excursions into the subject. Our viewpoint is biased by our experience in approximation theory and data compression; we wa
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PRABAKARAN, S., R. SAHU, and S. VERMA. "A WAVELET APPROACH FOR CLASSIFICATION OF MICROARRAY DATA." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (2008): 375–89. http://dx.doi.org/10.1142/s0219691308002409.

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Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal pr
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MAITY, SANTI P., and MALAY K. KUNDU. "PERFORMANCE IMPROVEMENT IN SPREAD SPECTRUM IMAGE WATERMARKING USING WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 01 (2011): 1–33. http://dx.doi.org/10.1142/s0219691311003931.

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This paper investigates the scope of wavelets for performance improvement in spread spectrum image watermarking. Performance of a digital image watermarking algorithm, in general, is determined by the visual invisibility of the hidden data (imperceptibility), reliability in the detection of the hidden information after various common and deliberate signal processing operations (robustness) applied on the watermarked signals and the amount of data to be hidden (payload) without affecting the imperceptibility and robustness properties. In this paper, we propose a few spread spectrum (SS) image w
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AZAD, SARITA, R. NARASIMHA, and S. K. SETT. "MULTIRESOLUTION ANALYSIS FOR SEPARATING CLOSELY SPACED FREQUENCIES WITH AN APPLICATION TO INDIAN MONSOON RAINFALL DATA." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 05 (2007): 735–52. http://dx.doi.org/10.1142/s0219691307002026.

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In this paper we make use of the multiresolution properties of discrete wavelets, including their ability to remove interference, to reveal closely spaced spectral peaks. We propose a procedure which we first verify on two test signals, and then apply it to the time series of homogeneous Indian monsoon rainfall annual data. We show that, compared to empirical mode decomposition, discrete wavelet analysis is more effective in identifying closely spaced frequencies if used in combination with classical power spectral analysis of wavelet-based partially reconstructed time series. An effective cri
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JIANG, QINGTANG. "BIORTHOGONAL WAVELETS WITH SIX-FOLD AXIAL SYMMETRY FOR HEXAGONAL DATA AND TRIANGLE SURFACE MULTIRESOLUTION PROCESSING." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 05 (2011): 773–812. http://dx.doi.org/10.1142/s0219691311004316.

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This paper discusses the construction of highly symmetric compactly supported wavelets for hexagonal data/image and triangle surface multiresolution processing. Recently, hexagonal image processing has attracted attention. Compared with the conventional square lattice, the hexagonal lattice has several advantages, including that it has higher symmetry. It is desirable that the filter banks for hexagonal data also have high symmetry which is pertinent to the symmetric structure of the hexagonal lattice. The high symmetry of filter banks and wavelets not only leads to simpler algorithms and effi
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Brysina, Iryna Victorivna, and Victor Olexandrovych Makarichev. "GENERALIZED ATOMIC WAVELETS." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (February 23, 2018): 23–31. http://dx.doi.org/10.32620/reks.2018.1.03.

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The problem of big data sets processing is considered. Efficiency of algorithms depends mainly on the appropriate mathematical tools. Now there exists a wide variety of different constructive tools for information analysis. Atomic functions are one of them. Theory of atomic functions was developed by V. A. Rvachev and members of his scientific school. A number of results, which prove that application of atomic functions is reasonable, were obtained. In particular, atomic functions are infinitely differentiable. This property is quite useful for smooth data processing (for example, color photos
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Dissertations / Theses on the topic "Wavelets (Mathematics) – Data processing"

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Cena, Bernard Maria. "Reconstruction for visualisation of discrete data fields using wavelet signal processing." University of Western Australia. Dept. of Computer Science, 2000. http://theses.library.uwa.edu.au/adt-WU2003.0014.

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The reconstruction of a function and its derivative from a set of measured samples is a fundamental operation in visualisation. Multiresolution techniques, such as wavelet signal processing, are instrumental in improving the performance and algorithm design for data analysis, filtering and processing. This dissertation explores the possibilities of combining traditional multiresolution analysis and processing features of wavelets with the design of appropriate filters for reconstruction of sampled data. On the one hand, a multiresolution system allows data feature detection, analysis and fil
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Chen, Shuo. "MALDI-TOF MS data processing using wavelets, splines and clustering techniques." [Johnson City, Tenn. : East Tennessee State University], 2004. http://etd-submit.etsu.edu/etd/theses/available/etd-1112104-113123/unrestricted/ChenS121404f.pdf.

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Thesis (M.S.)--East Tennessee State University, 2004.<br>Title from electronic submission form. ETSU ETD database URN: etd-1112104-113123 Includes bibliographical references. Also available via Internet at the UMI web site.
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Jung, Uk. "Wavelet-based Data Reduction and Mining for Multiple Functional Data." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5084.

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Advance technology such as various types of automatic data acquisitions, management, and networking systems has created a tremendous capability for managers to access valuable production information to improve their operation quality and efficiency. Signal processing and data mining techniques are more popular than ever in many fields including intelligent manufacturing. As data sets increase in size, their exploration, manipulation, and analysis become more complicated and resource consuming. Timely synthesized information such as functional data is needed for product design, process trouble-
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Leite, Ricardo Barroso 1984. "Compressão de imagens digitais combinando técnicas wavelet e wedgelet no ambiente de comunicações móveis." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260074.

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Orientadores: Yuzo Iano, Ana Lúcia Mendes Cruz Silvestre da Silva<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-18T18:35:20Z (GMT). No. of bitstreams: 1 Leite_RicardoBarroso_M.pdf: 4189561 bytes, checksum: 31cbbc85c9fa0ec77e54c0fdfac29e3f (MD5) Previous issue date: 2011<br>Resumo: Os avanços em telecomunicações e o desenvolvimento dos equipamentos digitais impulsionaram diversas áreas de pesquisa relacionadas à codificação e compressão de imagens. Dentre as áreas de atuação destacam-se as
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Hua, Li. "Vector wavelet transforms for the coding of static and time-varying vector fields." Diss., Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-05062003-120341.

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De, Voir Christopher S. "Wavelet Based Feature Extraction and Dimension Reduction for the Classification of Human Cardiac Electrogram Depolarization Waveforms." PDXScholar, 2005. https://pdxscholar.library.pdx.edu/open_access_etds/1740.

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An essential task for a pacemaker or implantable defibrillator is the accurate identification of rhythm categories so that the correct electrotherapy can be administered. Because some rhythms cause a rapid dangerous drop in cardiac output, it is necessary to categorize depolarization waveforms on a beat-to-beat basis to accomplish rhythm classification as rapidly as possible. In this thesis, a depolarization waveform classifier based on the Lifting Line Wavelet Transform is described. It overcomes problems in existing rate-based event classifiers; namely, (1) they are insensitive to the conduc
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Silva, Carlos Alexandre Moreira da 1984. "Aplicação de tecnologias analíticas de processo e inteligência artificial para monitoramento e controle de processo de recobrimento de partículas em leito fluidizado." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266036.

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Orientador: Osvaldir Pereira Taranto<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química<br>Made available in DSpace on 2018-08-27T00:40:14Z (GMT). No. of bitstreams: 1 Silva_CarlosAlexandreMoreirada_D.pdf: 33350422 bytes, checksum: 046e0a2c090474593621166c81042136 (MD5) Previous issue date: 2015<br>Resumo: As indústrias química, alimentícia e farmacêutica têm empregado extensivamente a operação de fluidização em inúmeros processos, devido às suas características bastante atrativas, que possibilitam um contato efetivo entre a fase sólida e fluida, o que r
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Zhao, Fangwei. "Multiresolution analysis of ultrasound images of the prostate." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0028.

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[Truncated abstract] Transrectal ultrasound (TRUS) has become the urologist’s primary tool for diagnosing and staging prostate cancer due to its real-time and non-invasive nature, low cost, and minimal discomfort. However, the interpretation of a prostate ultrasound image depends critically on the experience and expertise of a urologist and is still difficult and subjective. To overcome the subjective interpretation and facilitate objective diagnosis, computer aided analysis of ultrasound images of the prostate would be very helpful. Computer aided analysis of images may improve diagnostic ac
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Shah, Vijay Pravin. "A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information mining." Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-07062007-134150.

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Hloupis, Georgios. "Seismological data acquisition and signal processing using wavelets." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3470.

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This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite lin
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Books on the topic "Wavelets (Mathematics) – Data processing"

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Starck, J. L. Image processing and data analysis: The multiscale approach. Cambridge University Press, 1998.

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Hong-Ye, Gao, ed. Applied wavelet analysis with S-plus. Springer, 1996.

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1963-, Kunoth Angela, and SpringerLink (Online service), eds. Multiscale, Nonlinear and Adaptive Approximation: Dedicated to Wolfgang Dahmen on the Occasion of his 60th Birthday. Springer-Verlag Berlin Heidelberg, 2009.

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Brenner, Martin J. On-line robust modal stability prediction using wavelet processing. National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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P, Dikshit H., and Micchelli Charles A, eds. Proceedings of the Conference on Advances in Computational Mathematics: New Delhi, India, January 5-9, 1993. World Scientific, 1994.

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Glabisz, Wojciech. Pakietowa analiza falkowa w zagadnieniach mechaniki. Oficyna Wydawnicza Politechniki Wrocławskiej, 2004.

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Resnikoff, Howard L. Wavelet Analysis: The Scalable Structure of Information. Springer New York, 1998.

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E, Newland D., and Newland D. E, eds. An introduction to random vibrations, spectral and wavelet analysis. 3rd ed. Longman Scientific & Technical, 1993.

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Xiaomo, Jiang, ed. Intelligent infrastructure: Neural networks, wavelets, and chaos theory for intelligent transportation systems and smart structures. CRC Press, 2008.

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Computational signal processing with wavelets. Birkhäuser, 1998.

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Book chapters on the topic "Wavelets (Mathematics) – Data processing"

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Acevedo, Liesner, Victor M. Garcia, Antonio M. Vidal, and Pedro Alonso. "Partial Data Replication as a Strategy for Parallel Computing of the Multilevel Discrete Wavelet Transform." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14390-8_6.

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Dyke, Phil. "Wavelets and Signal Processing." In Springer Undergraduate Mathematics Series. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6395-4_7.

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Moraru, Luminița, Simona Moldovanu, Salam Khan, and Anjan Biswas. "Digital Image Processing Using Wavelets." In Applied Machine Learning for Smart Data Analysis. CRC Press, 2019. http://dx.doi.org/10.1201/9780429440953-4.

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Mizohata, Kiyoshi. "The Analysis of Big Data by Wavelets." In Trends in Mathematics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48812-7_74.

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Gorawski, Marcin, and Pawel Marks. "Resumption of Data Extraction Process in Parallel Data Warehouses." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_58.

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Gorawski, Marcin, and Rafal Malczok. "Distributed Spatial Data Warehouse." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24669-5_88.

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Kwedlo, Wojciech. "Parallelizing Evolutionary Algorithms for Clustering Data." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_52.

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Słota, Renata, Darin Nikolow, Marcin Kuta, et al. "Replica Management for National Data Storage." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14403-5_20.

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Fox, Geoffrey C., Mehmet S. Aktas, Galip Aydin, et al. "Grids for Real Time Data Applications." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_39.

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Cai, Min, Istvan Jonyer, and Marcin Paprzycki. "Improving Parallelism in Structural Data Mining." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_55.

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Conference papers on the topic "Wavelets (Mathematics) – Data processing"

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Babin, Andrey V., and Elena D. Kotina. "Mathematical data processing of gated spect myocardial perfusion imaging with using wavelet analysis." In 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA). IEEE, 2014. http://dx.doi.org/10.1109/icctpea.2014.6893254.

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Dunaeva, Ksenia, and Olga Sagaidachnya. "Building of special wavelets for processing of seismic data." In SEG Technical Program Expanded Abstracts 2007. Society of Exploration Geophysicists, 2007. http://dx.doi.org/10.1190/1.2793031.

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Wang, Qingzheng, Xiaokang Yang, Huixin Wu, and Xuemei Liu. "Anisotropic Data-Specific Wavelets for Structure-aware Image Processing." In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020. http://dx.doi.org/10.1109/icsip49896.2020.9339397.

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Oltean, Marius, and Miranda Nafornita. "Efficient Pulse Shaping and Robust Data Transmission Using Wavelets." In 2007 IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/wisp.2007.4447628.

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Kishore, P. V. V., A. S. C. S. Sastry, C. B. S. Vamsi Krishna, Y. Vikas, and C. S. D. Aneesh. "Hyperspectral face data reduction and classification with multiresolution wavelets." In 2015 International Conference on Signal Processing And Communication Engineering Systems (SPACES). IEEE, 2015. http://dx.doi.org/10.1109/spaces.2015.7058261.

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Sofi, Shabir Ahmad, and Roohie Naaz. "Data compression in Wireless visual Sensor networks using wavelets." In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016. http://dx.doi.org/10.1109/wispnet.2016.7566344.

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Gentilhomme, T., T. Mannseth, D. Oliver, G. Caumon, and R. Moyen. "Smooth Multi-scale Parameterization for Integration of Seismic and Production Data Using Second-generation Wavelets." In ECMOR XIII - 13th European Conference on the Mathematics of Oil Recovery. EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20143175.

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Nusantari, Diah Oga, Deni Nasir Ahmad, and Ihwan Zulkarnain. "Community Service: Processing Data Statistically." In SEMANTIK Conference of Mathematics Education (SEMANTIK 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200827.108.

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Schwarz, Gottfried, and Mihai P. Datcu. "Wavelets: a universal tool for the processing of remote sensing data?" In Aerospace Remote Sensing '97, edited by Jacky Desachy and Shahram Tajbakhsh. SPIE, 1997. http://dx.doi.org/10.1117/12.295630.

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Michielin, F., G. Calvagno, P. Sartor, and O. Erdler. "A wavelets based de-ringing technique for DCT based compressed visual data." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738227.

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Reports on the topic "Wavelets (Mathematics) – Data processing"

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Stirman, Charles. Applications of Wavelets to Radar Data Processing. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada239297.

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