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

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

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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 (September 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 utilized. Training data are used to construct probability distributions required for the computation of the entropy and mutual information. In our data analysis, we have used machine data acquired from a single cylinder engine under a series of induced faults in a test environment. The objective of the experiment was to evaluate the performance of the informative wavelet algorithm for the accuracy of classification results using a real-world machine data and to examine to what extent the results were influenced by different analyzing wavelets chosen for data analysis. Accuracy of classification results as related to the correlation structure of the coefficients is also discussed in the paper.
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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 (September 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 (June 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 wavelets having low data communication property such as the Haar wavelet are expected to be suitable for the purpose of improving computing performance. In this study, we also pay attention to the basic characteristics of the Haar-wavelet-based preconditioning method for a Poisson equation solver.
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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 (September 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 thresholding of the form [Formula: see text], where σ is minimal variance of the sum of packet decomposition of chosen level. 3. Cluster analysis of decomposed data. All programs were elaborated using program MATLAB 5.
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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 warn the reader that there are other viewpoints that are either not represented here or discussed only briefly. For example, orthogonal wavelets were developed primarily in the context of signal processing, an application upon which we touch only indirectly. However, there are several good expositions (e.g. Daubechies (1990) and Rioul and Vetterli (1991)) of this application. A discussion of wavelet decompositions in the context of Littlewood-Paley theory can be found in the monograph of Frazieret al. (1991). We shall also not attempt to give a complete discussion of the history of wavelets. Historical accounts can be found in the book of Meyer (1990) and the introduction of the article of Daubechies (1990). We shall try to give sufficient historical commentary in the course of our presentation to provide some feeling for the subject's development.
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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 (May 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 processing techniques are successful in many data processing techniques like image and video processing. But, the use of wavelets in analyzing the microarray bio-signals is not sufficiently probed. The aim of this paper is to propose a wavelet power spectrum based technique for dimensionality reduction through gene selection and classification problem of gene microarray data. The proposed method was administered on such datasets and the results are encouraging. The present method is robust to noise since no preprocessing has been applied. Also, it does not require any critical input parameters or any prior knowledge about the data which is required in many existing methods.
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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 (January 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 watermarking schemes using discrete wavelet transform (DWT), biorthogonal DWT and M-band wavelets coupled with various modulation, multiplexing and signaling techniques. The performance of the watermarking methods are also reported along with the relative merits and demerits.
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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 (September 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 criterion based on better localization of specific frequency components and accurate estimation of their amplitudes is used to select an appropriate wavelet. It is shown here that the discrete Meyer wavelet has the best frequency properties among the wavelet families considered (Haar, Daubechies, Coiflet and Symlet). In rainfall data, the present analysis reveals two additional spectral peaks besides the fifteen found by classical spectral analysis. Moreover, these two new peaks have been found to be statistically significant, although a detailed discussion of testing for significance is being presented elsewhere.
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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 (September 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 efficient computations, it also has the potential application for the texture segmentation of hexagonal data. While in the field of computer-aided geometric design (CAGD), when the filter banks are used for surface multiresolution processing, it is required that the corresponding decomposition and reconstruction algorithms for regular vertices have high symmetry, which make it possible to design the corresponding multiresolution algorithms for extraordinary vertices. In this paper we study the construction of six-fold axial symmetric biorthogonal filter banks and the associated wavelets, with both the dyadic and [Formula: see text]-refinements. The constructed filter banks have the desirable symmetry for hexagonal data processing. By associating the outputs (after one-level multiresolution decomposition) appropriately with the nodes of the regular triangular mesh with which the input data is associated (sampled), we represent multiresolution analysis and synthesis algorithms as templates. The six-fold axial symmetric filter banks constructed in this paper result in algorithm templates with desirable symmetry for triangle surface processing.
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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). Also, these functions have a local support, which allows to decrease complexity of numerical algorithms. Besides, it was shown that spaces of atomic functions have good approximation properties, which can reduce the error of computations. Hence, application of atomic functions is perspective. There are different ways to use atomic functions and their generalizations in practice. One such approach is a construction and application of wavelet-like structures. In this paper, generalized atomic wavelets are constructed using generalized Fup-functions and formulas for their evaluation are obtained. Also, the main properties of generalized atomic wavelets are presented. In addition, it is shown that these wavelets are smooth functions with a local support and have good approximation properties. Furthermore, the set of generalized atomic wavelets is a wide class of functions with flexible parameters that can be chosen according to specific needs. This means that the constructive analysis tool, which is introduced in this paper, gives researches and developers of algorithms flexible possibilities of adapting to the specifics of various problems. In addition, the problem of representation of data using generalized atomic wavelets is considered. Generalized atomic wavelets expansion of data is introduced. Such an expansion is a sum of trend or principal value function and several functions that describe the corresponding frequencies. The remainder term, which is an error of approximation of data by generalized atomic wavelets, is small. To estimate its value the inequalities from the previous papers of V. A. Rvachev, V. O. Makarichev and I. V. Brysina can be used
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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 filtering. Wavelets have already been proven successful in these tasks. On the other hand, a choice of discrete filter which converges to a continuous basis function under iteration permits efficient and accurate function representation by providing a “bridge” from the discrete to the continuous. A function representation method capable of both multiresolution analysis and accurate reconstruction of the underlying measured function would make a valuable tool for scientific visualisation. The aim of this dissertation is not to try to outperform existing filters designed specifically for reconstruction of sampled functions. The goal is to design a wavelet filter family which, while retaining properties necessary to preform multiresolution analysis, possesses features to enable the wavelets to be used as efficient and accurate “building blocks” for function representation. The application to visualisation is used as a means of practical demonstration of the results. Wavelet and visualisation filter design is analysed in the first part of this dissertation and a list of wavelet filter design criteria for visualisation is collated. Candidate wavelet filters are constructed based on a parameter space search of the BC-spline family and direct solution of equations describing filter properties. Further, a biorthogonal wavelet filter family is constructed based on point and average interpolating subdivision and using the lifting scheme. The main feature of these filters is their ability to reconstruct arbitrary degree piecewise polynomial functions and their derivatives using measured samples as direct input into a wavelet transform. The lifting scheme provides an intuitive, interval-adapted, time-domain filter and transform construction method. A generalised factorisation for arbitrary primal and dual order point and average interpolating filters is a result of the lifting construction. The proposed visualisation filter family is analysed quantitatively and qualitatively in the final part of the dissertation. Results from wavelet theory are used in the analysis which allow comparisons among wavelet filter families and between wavelets and filters designed specifically for reconstruction for visualisation. Lastly, the performance of the constructed wavelet filters is demonstrated in the visualisation context. One-dimensional signals are used to illustrate reconstruction performance of the wavelet filter family from noiseless and noisy samples in comparison to other wavelet filters and dedicated visualisation filters. The proposed wavelet filters converge to basis functions capable of reproducing functions that can be represented locally by arbitrary order piecewise polynomials. They are interpolating, smooth and provide asymptotically optimal reconstruction in the case when samples are used directly as wavelet coefficients. The reconstruction performance of the proposed wavelet filter family approaches that of continuous spatial domain filters designed specifically for reconstruction for visualisation. This is achieved in addition to retaining multiresolution analysis and processing properties of wavelets.
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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.
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-shooting, quality/efficiency improvement and resource allocation decisions. A major obstacle in those intelligent manufacturing system is that tools for processing a large volume of information coming from numerous stages on manufacturing operations are not available. Thus, the underlying theme of this thesis is to reduce the size of data in a mathematical rigorous framework, and apply existing or new procedures to the reduced-size data for various decision-making purposes. This thesis, first, proposes {it Wavelet-based Random-effect Model} which can generate multiple functional data signals which have wide fluctuations(between-signal variations) in the time domain. The random-effect wavelet atom position in the model has {it locally focused impact} which can be distinguished from other traditional random-effect models in biological field. For the data-size reduction, in order to deal with heterogeneously selected wavelet coefficients for different single curves, this thesis introduces the newly-defined {it Wavelet Vertical Energy} metric of multiple curves and utilizes it for the efficient data reduction method. The newly proposed method in this thesis will select important positions for the whole set of multiple curves by comparison between every vertical energy metrics and a threshold ({it Vertical Energy Threshold; VET}) which will be optimally decided based on an objective function. The objective function balances the reconstruction error against a data reduction ratio. Based on class membership information of each signal obtained, this thesis proposes the {it Vertical Group-Wise Threshold} method to increase the discriminative capability of the reduced-size data so that the reduced data set retains salient differences between classes as much as possible. A real-life example (Tonnage data) shows our proposed method is promising.
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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
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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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 aplicações para dispositivos móveis (celulares, smartphones, iPhones, iPads entre outros), que se caracterizam por baixas taxas de transmissão de dados. Entretanto, imagens codificadas com os padrões atualmente em estado-da-arte apresentam artefatos visuais característicos, como efeito de bloco e ringing. Para contornar a inabilidade das transformadas ortogonais em lidar com a geometria, é proposto na literatura o uso de dicionários wedgelet e da decomposição cartoon-textura. Nesse contexto, propõe-se um método de codificação híbrido wedgelet-wavelet inédito que preserva componentes de cartoon e textura, superando em qualidade visual ao uso de dicionários isolados e se aproximando do desempenho de sistemas de codificação completos, tais como o padrão JPEG 2000. Os ganhos de desempenho, principalmente em qualidade visual das imagens reconstruídas para baixas taxas de dados, indicam que a metodologia apresentada pode vir a ser incluída em sistemas de transmissão com restrições de largura de banda, como por exemplo a TV digital móvel
Abstract: Advances in telecommunications and the development of digital equipments have improved several research areas related to coding and image compression. Among these application fields are the mobile devices (cellphones, smartphones, iPhones, iPad, and others), characterized by low data transmission rates. However, images encoded by state-of-the-art standards present characteristic visual artifacts, like blocking and ringing effects. To surpass the disadvantages of orthogonal transforms in dealing with geometry, wedgelets dictionaries and cartoon-texture decomposition are proposed in literature. In this context, a new hybrid wedgelet-wavelet coding method that preserves cartoon and texture components is proposed, achieving better visual quality than the use of isolated dictionaries, approaching the performance of complete codification systems, such as the JPEG 2000. The performance gains, especially concerning visual quality of the reconstructed images using low data rates, show that this methodology might be adopted in restricted bandwidth transmission systems, such as the digital mobile TV
Mestrado
Telecomunicações e Telemática
Mestre em Engenharia Elétrica
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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 conduction path of the heart rhythm and (2) they are not robust to pseudo-events. The performance of the Lifting Line Wavelet Transform based classifier is illustrated with representative examples. Although rate based methods of event categorization have served well in implanted devices, these methods suffer in sensitivity and specificity when atrial, and ventricular rates are similar. Human experts differentiate rhythms by morphological features of strip chart electrocardiograms. The wavelet transform is a simple approximation of this human expert analysis function because it correlates distinct morphological features at multiple scales. The accuracy of implanted rhythm determination can then be improved by using human-appreciable time domain features enhanced by time scale decomposition of depolarization waveforms. The purpose of the present work was to determine the feasibility of implementing such a system on a limited-resolution platform. 78 patient recordings were split into equal segments of reference, confirmation, and evaluation sets. Each recording had a sampling rate of 512Hz, and a significant change in rhythm in the recording. The wavelet feature generator implemented in Matlab performs anti-alias pre-filtering, quantization, and threshold-based event detection, to produce indications of events to submit to wavelet transformation. The receiver operating characteristic curve was used to rank the discriminating power of the feature accomplishing dimension reduction. Accuracy was used to confirm the feature choice. Evaluation accuracy was greater than or equal to 95% over the IEGM recordings.
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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
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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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 reflete na geração de altas taxas de transferência de calor e de massa. No entanto, o regime de fluidização borbulhante, o qual é condição de partida dos processos que envolvem esta operação, frequentemente é afetado pelas condições operacionais. As temperaturas elevadas, o conteúdo de umidade excessivo das partículas e a introdução de líquidos no leito fluidizado podem conduzir a instabilidades no regime fluidodinâmico e provocar o colapso parcial ou total do leito, reduzindo a eficiência do processo. A manutenção de condições estáveis do regime de fluidização durante processos de recobrimento de partículas em leitos fluidizados é de fundamental importância para garantir uma eficiência de recobrimento favorável e evitar a formação de zonas sem movimentação e aglomeração das partículas no leito, pois estes fatores indesejáveis comprometem a mistura entre as fases e conseqüentemente a qualidade do produto final. Dentro deste contexto, a utilização de um sistema de monitoramento e controle em tempo real de processos de recobrimento de partículas é extremamente desejável para permitir a operação de regimes de fluidização estáveis e garantir um filme de recobrimento uniforme e boas condições de escoabilidade dos sólidos. A presente proposta de tese de doutorado tem por objetivo aplicar a metodologia de análise espectral Gaussiana dos sinais de flutuação de pressão (Parise et al. (2008)), para o desenvolvimento de sistemas de controle baseados em inteligência artificial (Lógica Fuzzy), visando monitorar a estabilidade do regime de fluidização em processo de recobrimento de partículas. Comparações entre as condições fluidodinâmicas dos processos com e sem controle foram analisadas para operações em leito fluidizado em escala de laboratorio. Para avaliar a qualidade das partículas foi utilizada uma sonda de monitoramento in-line (Parsum IPP70), onde se pôde verificar os instantes iniciais da aglomeração indesejada. Com a aplicação desde sistema automatizado foi possível associar a estabilidade da fluidização em função do elevado grau de aglomeração. O ponto de parada do processo pôde ser definido em 420 µm (inicial em 360 µm) e a partir deste o mecanismo de recobrimento acontece simultaneamente com o de aglomeração. Os parâmetros de monitoramento do regime conseguiram não somente identificar a fase inicial da defluidização, como também foi possível a partir deles, controlar o processo por Lógica Fuzzy-PI e estabilizar a operação para altas taxas de suspensão atomizadas
Abstract: The chemical, food and pharmaceutical industries have extensively used fluidization operation in many cases, due to its very attractive features that enable effective contact between the solid and fluid phase, which reflects the generation of high heat and mass transfer rates. However, the bubbling fluidization regime, which is the starting condition of the processes involved in this operation is often affected by operating conditions. Elevated temperatures, excessive moisture content of the particles and introduction of liquid into the fluidized bed may lead to instabilities in the fluid-dynamic regime and cause partial or total collapse of the bed, reducing the process efficiency. The maintenance of stable conditions of the fluidization regime for particle coating processes in fluidized beds is of fundamental importance to ensure a favorable coating efficiency and to avoid zones without movement and agglomeration of particles in the bed, because these undesirable factors compromise the mixing between the phases and therefore the quality of the final product. Within this context, the use of a monitoring system and real-time control of particle coating processes is highly desirable to allow operation in stable fluidization regimes and to ensure a uniform coating film and good condition of flowability of the solids. This doctoral thesis aims to apply the Gaussian spectral analysis methodology of the pressure fluctuation signals (Parise et al. (2008)) , for the development of control systems based on artificial intelligence (Fuzzy Logic), to monitor the stability of fluidization regime particle coating process. Comparisons between the fluid dynamic conditions of the processes with and without control were analyzed for operations in fluidized bed laboratory scale. To assess early stages of unwanted agglomeration, a monitoring in-line probe (Parsum IPP70) was used. With the application of this automated system, it was possible to associate the stability of fluidization with a high degree of agglomeration. The process stopping point could be set at 420 µm (initial in 360 µm) and after, the coating mechanism takes place simultaneously with the agglomeration one. The monitoring parameters of the system were able to identify the initial phase of defluidization, as well as it was possible to control the process by using Fuzzy Logic and to stabilize the operation for high rates of the coating suspension atomized onto the bed
Doutorado
Engenharia de Processos
Doutor em Engenharia Química
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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 accuracy by providing a more reproducible interpretation of the images. This thesis is an attempt to address several key elements of computer aided analysis of ultrasound images of the prostate. Specifically, it addresses the following tasks: 1. modelling B-mode ultrasound image formation and statistical properties; 2. reducing ultrasound speckle; and 3. extracting prostate contour. Speckle refers to the granular appearance that compromises the image quality and resolution in optics, synthetic aperture radar (SAR), and ultrasound. Due to the existence of speckle the appearance of a B-mode ultrasound image does not necessarily relate to the internal structure of the object being scanned. A computer simulation of B-mode ultrasound imaging is presented, which not only provides an insight into the nature of speckle, but also a viable test-bed for any ultrasound speckle reduction methods. Motivated by analysis of the statistical properties of the simulated images, the generalised Fisher-Tippett distribution is empirically proposed to analyse statistical properties of ultrasound images of the prostate. A speckle reduction scheme is then presented, which is based on Mallat and Zhong’s dyadic wavelet transform (MZDWT) and modelling statistical properties of the wavelet coefficients and exploiting their inter-scale correlation. Specifically, the squared modulus of the component wavelet coefficients are modelled as a two-state Gamma mixture. Interscale correlation is exploited by taking the harmonic mean of the posterior probability functions, which are derived from the Gamma mixture. This noise reduction scheme is applied to both simulated and real ultrasound images, and its performance is quite satisfactory in that the important features of the original noise corrupted image are preserved while most of the speckle noise is removed successfully. It is also evaluated both qualitatively and quantitatively by comparing it with median, Wiener, and Lee filters, and the results revealed that it surpasses all these filters. A novel contour extraction scheme (CES), which fuses MZDWT and snakes, is proposed on the basis of multiresolution analysis (MRA). Extraction of the prostate contour is placed in a multi-scale framework provided by MZDWT. Specifically, the external potential functions of the snake are designated as the modulus of the wavelet coefficients at different scales, and thus are “switchable”. Such a multi-scale snake, which deforms and migrates from coarse to fine scales, eventually extracts the contour of the prostate
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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 links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.
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Books on the topic "Wavelets (Mathematics) – Data processing"

1

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

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Hong-Ye, Gao, ed. Applied wavelet analysis with S-plus. New York: 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. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Brenner, Martin J. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: 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. Singapore: World Scientific, 1994.

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

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Resnikoff, Howard L. Wavelet Analysis: The Scalable Structure of Information. New York, NY: 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. Harlow, Essex, England: 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. Boca Raton, FL: CRC Press, 2008.

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Computational signal processing with wavelets. Boston: 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, 51–60. Berlin, Heidelberg: 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, 175–208. London: 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, 71–96. First edition. | New York, NY : CRC Press/Taylor & Francis Group, 2019. | Series: Computational Intelligence in Engineering Problem Solving: 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, 589–93. Cham: 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, 478–85. Berlin, Heidelberg: 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, 676–81. Berlin, Heidelberg: 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, 430–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_52.

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Słota, Renata, Darin Nikolow, Marcin Kuta, Mariusz Kapanowski, Kornel Skałkowski, Marek Pogoda, and Jacek Kitowski. "Replica Management for National Data Storage." In Parallel Processing and Applied Mathematics, 184–93. Berlin, Heidelberg: 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, Hasan Bulut, Harshawardhan Gadgil, Sangyoon Oh, Shrideep Pallickara, Marlon E. Pierce, Ahmet Sayar, and Gang Zhai. "Grids for Real Time Data Applications." In Parallel Processing and Applied Mathematics, 320–32. Berlin, Heidelberg: 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, 455–62. Berlin, Heidelberg: 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"

1

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. Netherlands: 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). Paris, France: 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"

1

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

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