Bibliografías temáticas / WAVELETE DOMAIN

Literatura académica sobre el tema "WAVELETE DOMAIN"

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Publicado: 11 de septiembre de 2023

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Artículos de revistas sobre el tema "WAVELETE DOMAIN"

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Toda, Hiroshi, Zhong Zhang y Takashi Imamura. "Perfect-translation-invariant variable-density complex discrete wavelet transform". International Journal of Wavelets, Multiresolution and Information Processing 12, n.º 04 (julio de 2014): 1460001. http://dx.doi.org/10.1142/s0219691314600017.

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The theorems giving the conditions for discrete wavelet transforms (DWTs) to achieve perfect translation invariance (PTI) have already been proven, and based on these theorems, the dual-tree complex DWT and the complex wavelet packet transform, achieving PTI, have already been proposed. However, there is not so much flexibility in their wavelet density. In the frequency domain, the wavelet density is fixed by octave filter banks, and in the time domain, each wavelet is arrayed on a fixed coordinate, and the wavelet packet density in the frequency domain can be only designed by dividing an octave frequency band equally in linear scale, and its density in the time domain is constrained by the division number of an octave frequency band. In this paper, a novel complex DWT is proposed to create variable wavelet density in the frequency and time domains, that is, an octave frequency band can be divided into N filter banks in logarithmic scale, where N is an integer larger than or equal to 3, and in the time domain, a distance between wavelets can be varied in each level, and its transform achieves PTI.
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TODA, HIROSHI, ZHONG ZHANG y TAKASHI IMAMURA. "PERFECT-TRANSLATION-INVARIANT CUSTOMIZABLE COMPLEX DISCRETE WAVELET TRANSFORM". International Journal of Wavelets, Multiresolution and Information Processing 11, n.º 04 (julio de 2013): 1360003. http://dx.doi.org/10.1142/s0219691313600035.

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The theorems, giving the condition of perfect translation invariance for discrete wavelet transforms, have already been proven. Based on these theorems, the dual-tree complex discrete wavelet transform, the 2-dimensional discrete wavelet transform, the complex wavelet packet transform, the variable-density complex discrete wavelet transform and the real-valued discrete wavelet transform, having perfect translation invariance, were proposed. However, their customizability of wavelets in the frequency domain is limited. In this paper, also based on these theorems, a new type of complex discrete wavelet transform is proposed, which achieves perfect translation invariance with high degree of customizability of wavelets in the frequency domain.
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3

Zhang, Jie, Xuehua Chen, Wei Jiang, Yunfei Liu y He Xu. "Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model". GEOPHYSICS 87, n.º 2 (24 de enero de 2022): R213—R222. http://dx.doi.org/10.1190/geo2020-0745.1.

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Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with increasing medium velocity and compresses with decreasing medium velocity. The commonly used convolutional model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We have developed a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well-log data. Our method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolutional model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. In addition, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares (LS)-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with LS-based methods. The results indicate that our method is effective and stable even for data with a low signal-to-noise ratio.
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Ďuriš, Viliam, Vladimir I. Semenov y Sergey G. Chumarov. "Wavelets and digital filters designed and synthesized in the time and frequency domains". Mathematical Biosciences and Engineering 19, n.º 3 (2022): 3056–68. http://dx.doi.org/10.3934/mbe.2022141.

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<abstract> <p>The relevance of the problem under study is due to the fact that the comparison is made for wavelets constructed in the time and frequency domains. The wavelets constructed in the time domain include all discrete wavelets, as well as continuous wavelets based on derivatives of the Gaussian function. This article discusses the possibility of implementing algorithms for multiscale analysis of one-dimensional and two-dimensional signals with the above-mentioned wavelets and wavelets constructed in the frequency domain. In contrast to the discrete wavelet transform (Mallat algorithm), the authors propose a multiscale analysis of images with a multiplicity of less than two in the frequency domain, that is, the scale change factor is less than 2. Despite the fact that the multiplicity of the analysis is less than 2, the signal can be represented as successive approximations, as with the use of discrete wavelet transform. Reducing the multiplicity allows you to increase the depth of decomposition, thereby increasing the accuracy of signal analysis and synthesis. At the same time, the number of decomposition levels is an order of magnitude higher compared to traditional multi-scale analysis, which is achieved by progressive scanning of the image, that is, the image is processed not by rows and columns, but by progressive scanning as a whole. The use of the fast Fourier transform reduces the conversion time by four orders of magnitude compared to direct numerical integration, and due to this, the decomposition and reconstruction time does not increase compared to the time of multiscale analysis using discrete wavelets.</p> </abstract>
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Abuhamdia, Tariq, Saied Taheri y John Burns. "Laplace wavelet transform theory and applications". Journal of Vibration and Control 24, n.º 9 (11 de mayo de 2017): 1600–1620. http://dx.doi.org/10.1177/1077546317707103.

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This study introduces the theory of the Laplace wavelet transform (LWT). The Laplace wavelets are a generalization of the second-order under damped linear time-invariant (SOULTI) wavelets to the complex domain. This generalization produces the mother wavelet function that has been used as the Laplace pseudo wavelet or the Laplace wavelet dictionary. The study shows that the Laplace wavelet can be used to transform signals to the time-scale or time-frequency domain and can be retrieved back. The properties of the new generalization are outlined, and the characteristics of the companion wavelet transform are defined. Moreover, some similarities between the Laplace wavelet transform and the Laplace transform arise, where a relation between the Laplace wavelet transform and the Laplace transform is derived. This relation can be beneficial in evaluating the wavelet transform. The new wavelet transform has phase and magnitude, and can also be evaluated for most elementary signals. The Laplace wavelets inherit many properties from the SOULTI wavelets, and the Laplace wavelet transform inherits many properties from both the SOULTI wavelet transform and the Laplace transform. In addition, the investigation shows that both the LWT and the SOULTI wavelet transform give the particular solutions of specific related differential equations, and the particular solution of these linear time-invariant differential equations can in general be written in terms of a wavelet transform. Finally, the properties of the Laplace wavelet are verified by applications to frequency varying signals and to vibrations of mechanical systems for modes decoupling, and the results are compared with the generalized Morse and Morlet wavelets in addition to the short time Fourier transform’s results.
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KING, EMILY J. "SMOOTH PARSEVAL FRAMES FOR L2(ℝ) AND GENERALIZATIONS TO L2(ℝd)". International Journal of Wavelets, Multiresolution and Information Processing 11, n.º 06 (noviembre de 2013): 1350047. http://dx.doi.org/10.1142/s0219691313500471.

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Wavelet set wavelets were the first examples of wavelets that may not have associated multiresolution analyses. Furthermore, they provided examples of complete orthonormal wavelet systems in L2(ℝd) which only require a single generating wavelet. Although work had been done to smooth these wavelets, which are by definition discontinuous on the frequency domain, nothing had been explicitly done over ℝd, d > 1. This paper, along with another one cowritten by the author, finally addresses this issue. Smoothing does not work as expected in higher dimensions. For example, Bin Han's proof of existence of Schwartz class functions which are Parseval frame wavelets and approximate Parseval frame wavelet set wavelets does not easily generalize to higher dimensions. However, a construction of wavelet sets in [Formula: see text] which may be smoothed is presented. Finally, it is shown that a commonly used class of functions cannot be the result of convolutional smoothing of a wavelet set wavelet.
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Bansal, Rishi y Mike Matheney. "Wavelet distortion correction due to domain conversion". GEOPHYSICS 75, n.º 6 (noviembre de 2010): V77—V87. http://dx.doi.org/10.1190/1.3494081.

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Converted-wave (PS) data, when converted to PP time, develop time- and location-varying compression of the seismic wavelet due to a variable subsurface [Formula: see text] [Formula: see text]. The time-dependent compression distorts the wavelet in a seismic trace. The lack of a consistent seismic wavelet in a domain-converted PS volume can eventually lead to an erroneous joint PP/PS inversion result. Depth-converted seismic data also have wavelet distortion due to velocity-dependent wavelet stretch. A high value of seismic velocity produces more stretch in a seismic wavelet than a low value. Variable wavelet stretch renders the depth data unsuitable for attribute analysis. A filtering scheme is proposed that corrects for distortion in seismic wavelets due to domain conversions (PS to PP time and time-to-depth) of seismic data in an amplitude-preserving manner. The method uses a Fourier scaling theorem to predict the seismic wavelet in the converted domain and calculates a shaping filter for each time/depth sample that corrects for the distortion in the wavelet. The filter is applied to the domain-converted data using the method of nonstationary filtering. We provide analytical expressions for the squeeze factor [Formula: see text] that is used to predict the wavelet in the converted domain. The squeeze factor [Formula: see text] for PS to PP time conversion is a function of the subsurface [Formula: see text] whereas for PP time-to-depth conversion [Formula: see text] is dependent on subsurface P-wave velocity. After filtering, the squeezed wavelets in domain-converted PS data appear to have resulted from a constant subsurface [Formula: see text], which we denote as [Formula: see text]. Similarly, the filtered depth-converted data appear to have resulted from a constant subsurface P-wave velocity [Formula: see text].
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8

Ďuriš, Viliam, Sergey G. Chumarov y Vladimir I. Semenov. "Increasing the Speed of Multiscale Signal Analysis in the Frequency Domain". Electronics 12, n.º 3 (2 de febrero de 2023): 745. http://dx.doi.org/10.3390/electronics12030745.

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In the Mallat algorithm, calculations are performed in the time domain. To speed up the signal conversion at each level, the wavelet coefficients are sequentially halved. This paper presents an algorithm for increasing the speed of multiscale signal analysis using fast Fourier transform. In this algorithm, calculations are performed in the frequency domain, which is why the authors call this algorithm multiscale analysis in the frequency domain. For each level of decomposition, the wavelet coefficients are determined from the signal and can be calculated in parallel, which reduces the conversion time. In addition, the zoom factor can be less than two. The Mallat algorithm uses non-symmetric wavelets, and to increase the accuracy of the reconstruction, large-order wavelets are obtained, which increases the transformation time. On the contrary, in our algorithm, depending on the sample length, the wavelets are symmetric and the time of the inverse wavelet transform can be faster by 6–7 orders of magnitude compared to the direct numerical calculation of the convolution. At the same time, the quality of analysis and the accuracy of signal reconstruction increase because the wavelet transform is strictly orthogonal.
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9

Sun, Song Zhen y Yi Guo. "Study of Periodic Frames and Trivariate Tight Wavelet Frames and Applications in Materials Engineering". Advanced Materials Research 1079-1080 (diciembre de 2014): 878–81. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.878.

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It is shown that there exists a frame wavelet with fast decay in the time domain and compact support in the frequency domain generating a wavelet system whose canonical dual frame cannot be generated by an arbitrary number of generators. We show that there exist wavelet frame generated by two functions which have good dual wavelet frames, but for which the canonical dual wavelet frame does not consist of wavelets, according to scaling functions.
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Qin, Jun y Pengfei Sun. "Applications and Comparison of Continuous Wavelet Transforms on Analysis of A-wave Impulse Noise". Archives of Acoustics 40, n.º 4 (1 de diciembre de 2015): 503–12. http://dx.doi.org/10.1515/aoa-2015-0050.

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Abstract Noise induced hearing loss (NIHL) is a serious occupational related health problem worldwide. The A-wave impulse noise could cause severe hearing loss, and characteristics of such kind of impulse noise in the joint time-frequency (T-F) domain are critical for evaluation of auditory hazard level. This study focuses on the analysis of A-wave impulse noise in the T-F domain using continual wavelet transforms. Three different wavelets, referring to Morlet, Mexican hat, and Meyer wavelets, were investigated and compared based on theoretical analysis and applications to experimental generated A-wave impulse noise signals. The underlying theory of continuous wavelet transform was given and the temporal and spectral resolutions were theoretically analyzed. The main results showed that the Mexican hat wavelet demonstrated significant advantages over the Morlet and Meyer wavelets for the characterization and analysis of the A-wave impulse noise. The results of this study provide useful information for applying wavelet transform on signal processing of the A-wave impulse noise.
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Tesis sobre el tema "WAVELETE DOMAIN"

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Souare, Moussa. "Sar Image Analysis In Wavelets Domain". Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405014006.

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Azimifar, Seyedeh-Zohreh. "Image Models for Wavelet Domain Statistics". Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/938.

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Statistical models for the joint statistics of image pixels are of central importance in many image processing applications. However the high dimensionality stemming from large problem size and the long-range spatial interactions make statistical image modeling particularly challenging. Commonly this modeling is simplified by a change of basis, mostly using a wavelet transform. Indeed, the wavelet transform has widely been used as an approximate whitener of statistical time series. It has, however, long been recognized that the wavelet coefficients are neither Gaussian, in terms of the marginal statistics, nor white, in terms of the joint statistics. The question of wavelet joint models is complicated and admits for possibilities, with statistical structures within subbands, across orientations, and scales. Although a variety of joint models have been proposed and tested, few models appear to be directly based on empirical studies of wavelet coefficient cross-statistics. Rather, they are based on intuitive or heuristic notions of wavelet neighborhood structures. Without an examination of the underlying statistics, such heuristic approaches necessarily leave unanswered questions of neighborhood sufficiency and necessity. This thesis presents an empirical study of joint wavelet statistics for textures and other imagery including dependencies across scale, space, and orientation. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find that wavelet spatial statistics are strongly orientation dependent, structures which are surprisingly not considered by state-of-the-art wavelet modeling techniques. To demonstrate the effectiveness of the studied wavelet correlation models a novel non-linear correlated empirical Bayesian shrinkage algorithm based on the wavelet joint statistics is proposed. In comparison with popular nonlinear shrinkage algorithms, it improves the denoising results.
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Temizel, Alptekin. "Wavelet domain image resolution enhancement methods". Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425928.

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Zhang, Xudong. "Wavelet-domain hyperspectral soil texture classification". Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-04012004-142420.

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Chanerley, Andrew A. "Seismic correction in the wavelet domain". Thesis, University of East London, 2014. http://roar.uel.ac.uk/4395/.

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This thesis summarises novel approaches and methods in the wavelet domain employed and published in the literature by the author for the correction and processing of time-series data from recorded seismic events, obtained from strong motion accelerographs. Historically, the research developed to first de-convolve the instrument response from legacy analogue strong-motion instruments, of which there are a large number. This was to make available better estimates of the acceleration ground motion before the more problematic part of the research that of obtaining ground velocities and displacements. The characteristics of legacy analogue strongmotion instruments are unfortunately in most cases not available, making it difficult to de-couple the instrument response. Essentially this is a system identification problem presented and summarised therein with solutions that are transparent to this lack of instrument data. This was followed by the more fundamental and problematic part of the research that of recovering the velocity and displacement from the recorded data. In all cases the instruments are tri-axial, i.e. translation only. This is a limiting factor and leads to distortions manifest by dc shifts in the recorded data as a consequence of the instrument pitching, rolling and yawing during seismic events. These distortions are embedded in the translation acceleration time–series, their contributions having been recorded by the same tri-axial sensors. In the literature this is termed ‘baseline error’ and it effectively prevents meaningful integration to velocity and displacement. Sophisticated methods do exist, which recover estimates of velocity and displacement, but these require a good measure of expertise and do not recover all the possible information from the recorded data. A novel, automated wavelet transform method developed by the author and published in the earthquake engineering literature is presented. This surmounts the problem of obtaining the velocity and displacement and in addition recovers both a low-frequency pulse called the ‘fling’, the displacement ‘fling-step’ and the form of the baseline error, both inferred in the literature, but hitherto never recovered. Once the acceleration fling pulse is recovered meaningful integration becomes a reality. However, the necessity of developing novel algorithms in order to recover important information emphasises the weakness of modern digital instruments in that they are all tri- rather than sextaxial instruments.
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Lebed, Evgeniy. "Sparse signal recovery in a transform domain". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4171.

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The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last thirty years many transforms such as wavelets, curvelets, contourlets, surfacelets, shearlets, and many other types of ‘x-lets’ have been developed. Such transform were leveraged to resolve this issue of sparse representations. In this work we compare the properties of four of these commonly used transforms, namely the shift-invariant wavelets, complex wavelets, curvelets and surfacelets. We also explore the performance of these transforms for the problem of recovering seismic wavefields from incomplete measurements.
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LOUREIRO, FELIPE PRADO. "ACOUSTIC MODELING IN THE WAVELET TRANSFORM DOMAIN". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=4915@1.

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PETRÓLEO BRASILEIRO S. A.
O processamento de sinais sísmicos é peça chave na exploração petrolífera. O caminho entre aquisição de dados e interpretação sísmica é composto por uma trilha de processos interdependentes, entre eles os processos de modelagem e migração. A dissertação apresenta a composição de um algoritmo de modelagem acústica 2D no domínio da transformada wavelet a partir de ferramentas próprias e outras já existentes na literatura. São estabelecidas as aproximações necessárias à solução em meios heterogêneos e à independência entre os subdomínios de processamento. Esta independência possibilita a exploração de técnicas de processamento paralelo. Através de exemplos, seu desempenho é avaliado com comparações à solução via diferenças finitas. Estas soluções são ainda submetidas ao mesmo processo de migração baseado em um terceiro modo de solução.
Seismic signal processing is a key step to oil exploration. The path between data acquisition and seismic interpretation is composed by a sequence of interdependent processes, among which are modeling and migration processes. A 2D acoustic modeling algorithm in wavelet Transform domain, based on custom tools and tools already made known in literature is presented. Approximations necessary for the solution in inhomogeneous media and for complete independence between processing subspaces are established. Such independence allows exploration of parallel processing techniques. Throughout examples, performance is evaluated in comparison to finite-difference solution. These solutions are further processed by a migration technique based in yet another solution method.
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Goda, Matthew. "Wavelet domain image restoration and super-resolution". Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/289808.

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Multi-resolution techniques, and especially the wavelet transform provide unique benefits in image representation and processing not otherwise possible. While wavelet applications in image compression and denoising have become extremely prevalent, their use in image restoration and super-resolution has not been exploited to the same degree. One issue is the extension 1-D wavelet transforms into 2-D via separable transforms versus the non-separability of typical circular aperture imaging systems. This mismatch leads to performance degradations. Image restoration, the inverse problem to image formation, is the first major focus of this research. A new multi-resolution transform is presented to improve performance. The transform is called a Radially Symmetric Discrete Wavelet-like Transform (RS-DWT) and is designed based on the non-separable blurring of the typical incoherent circular aperture imaging system. The results using this transform show marked improvement compared to other restoration algorithms both in Mean Square Error and visual appearance. Extensions to the general algorithm that further improve results are discussed. The ability to super-resolve imagery using wavelet-domain techniques is the second major focus of this research. Super-resolution, the ability to reconstruct object information lost in the imaging process, has been an active research area for many years. Multiple experiments are presented which demonstrate the possibilities and problems associated with super-resolution in the wavelet-domain. Finally, super-resolution in the wavelet domain using Non-Linear Interpolative Vector Quantization is studied and the results of the algorithm are presented and discussed.
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Ngadiran, Ruzelita. "Rate scalable image compression in the wavelet domain". Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1437.

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This thesis explores image compression in the wavelet transform domain. This the- sis considers progressive compression based on bit plane coding. The rst part of the thesis investigates the scalar quantisation technique for multidimensional images such as colour and multispectral image. Embedded coders such as SPIHT and SPECK are known to be very simple and e cient algorithms for compression in the wavelet do- main. However, these algorithms require the use of lists to keep track of partitioning processes, and such lists involve high memory requirement during the encoding process. A listless approach has been proposed for multispectral image compression in order to reduce the working memory required. The earlier listless coders are extended into three dimensional coder so that redundancy in the spectral domain can be exploited. Listless implementation requires a xed memory of 4 bits per pixel to represent the state of each transformed coe cient. The state is updated during coding based on test of sig- ni cance. Spectral redundancies are exploited to improve the performance of the coder by modifying its scanning rules and the initial marker/state. For colour images, this is done by conducting a joint the signi cant test for the chrominance planes. In this way, the similarities between the chrominance planes can be exploited during the cod- ing process. Fixed memory listless methods that exploit spectral redundancies enable e cient coding while maintaining rate scalability and progressive transmission. The second part of the thesis addresses image compression using directional filters in the wavelet domain. A directional lter is expected to improve the retention of edge and curve information during compression. Current implementations of hybrid wavelet and directional (HWD) lters improve the contour representation of compressed images, but su er from the pseudo-Gibbs phenomenon in the smooth regions of the images. A di erent approach to directional lters in the wavelet transforms is proposed to remove such artifacts while maintaining the ability to preserve contours and texture. Imple- mentation with grayscale images shows improvements in terms of distortion rates and the structural similarity, especially in images with contours. The proposed transform manages to preserve the directional capability without pseudo-Gibbs artifacts and at the same time reduces the complexity of wavelet transform with directional lter. Fur-ther investigation to colour images shows the transform able to preserve texture and curve.
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Avaritsioti, Eleni. "Financial time series prediction in the wavelet domain". Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502386.

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Libros sobre el tema "WAVELETE DOMAIN"

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Hesthaven, J. S. A wavelet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Function spaces and wavelets on domains. Zürich: European Mathematical Society, 2008.

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M, Jameson Leland y Langley Research Center, eds. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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M, Jameson Leland y Langley Research Center, eds. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Hesthaven, Jan S. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Rapp, R. Ocean domains and maximum degree of spherical harmonic and orthonormal expansions. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1999.

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Rapp, R. Ocean domains and maximum degree of spherical harmonic and orthonormal expansions. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1999.

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Castillejos, Heydy. Fuzzy Image Segmentation Algorithms in Wavelet Domain. INTECH Open Access Publisher, 2012.

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Jouini, Abdellatif. Wavelet Bases in Bounded Domains and Applications. Alpha Science International, Limited, 2018.

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Wavelet Domain Communication System (WDCS): Packet-Based Wavelet Spectral Estimation and M-ARY Signaling. Storming Media, 2002.

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Capítulos de libros sobre el tema "WAVELETE DOMAIN"

1

Chanerley, A. A. y N. A. Alexander. "Wavelet Domain Seismic Correction". En Encyclopedia of Earthquake Engineering, 1–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36197-5_272-1.

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Chanerley, A. A. y N. A. Alexander. "Wavelet Domain Seismic Correction". En Encyclopedia of Earthquake Engineering, 3934–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_272.

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Ling, Wing-kuen y P. K. S. Tam. "Reduction of Blocking Artifacts in Both Spatial Domain and Transformed Domain". En Wavelet Analysis and Its Applications, 37–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45333-4_7.

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Jansen, Maarten. "Dyadic wavelet design in the frequency domain". En Wavelets from a Statistical Perspective, 163–82. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003265375-6.

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Levesley, J. y M. Roach. "Quasi-Interpolation on Compact Domains". En Approximation Theory, Wavelets and Applications, 557–66. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8577-4_39.

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Vannucci, Marina y Fabio Corradi. "Modeling Dependence in the Wavelet Domain". En Bayesian Inference in Wavelet-Based Models, 173–86. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0567-8_12.

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Chipman, Hugh A. y Lara J. Wolfson. "Prior Elicitation in the Wavelet Domain". En Bayesian Inference in Wavelet-Based Models, 83–94. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0567-8_6.

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Singh, Rajiv, Swati Nigam, Amit Kumar Singh y Mohamed Elhoseny. "On Wavelet Domain Video Watermarking Techniques". En Intelligent Wavelet Based Techniques for Advanced Multimedia Applications, 65–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31873-4_5.

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Azimifar, Zohreh, Paul Fieguth y Ed Jernigan. "Textures and Wavelet-Domain Joint Statistics". En Lecture Notes in Computer Science, 331–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_41.

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Meyer, Stefan, Thomas Nowotny, Paul Graham, Alex Dewar y Andrew Philippides. "Snapshot Navigation in the Wavelet Domain". En Biomimetic and Biohybrid Systems, 245–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64313-3_24.

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Actas de conferencias sobre el tema "WAVELETE DOMAIN"

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de Moraes, Francisco José Vicente y Hans Ingo Weber. "Deconvolution by Wavelets for Extracting Impulse Response Functions". En ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4136.

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Abstract The extraction of Impulse Response Functions (Markov parameters) is a major feature on dynamic systems identification. The convolution integral is a most important input-output relationship for linear systems. Existing methods for calculating the IRFs from the convolution integral are carried out in time or frequency domains. The orthonormal wavelet transform consists on decomposing a given signal on mutually orthogonal local basis functions. It is possible to make use of the orthogonal properties of wavelets for calculating the convolution integral. The wavelet domain preserves the temporal nature of data and, simultaneously, different frequency bands are isolated by the multiresolution analysis, without loosing the orthogonality of the wavelet terms. Algorithm matrices are well conditioned and the method is not very sensitive to output noise. Simulated and experimental analysis are performed and results presented.
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Wirasaet, Damrongsak y Samuel Paolucci. "An Adaptive Wavelet Method for the Incompressible Navier-Stokes Equations in Complex Domains". En ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56317.

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An adaptive wavelet-based method provides an alternative means to refine grids according to local demands of the physical solution. One of the prominent challenges of such a method is the application to problems defined on complex domains. In the case of incompressible flow, the application to problems with complicated domains is made possible by the use of the Navier-Stokes/Brinkman equations. These equations take into account solid obstacles by adding a penalized velocity term in the momentum equation. In this study, an adaptive wavelet collocation method, based on interpolating wavelets, is first applied to a benchmark problem defined on a simple domain to demonstrate the accuracy and efficiency of the method. Then the penalty technique is used to simulate flows over obstacles. The numerical results are compared with those obtained by other computational approaches as well as with experiments.
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Semenov, Vladimir y Aleksandr Shurbin. "USING WAVELETS WITH A RECTANGULAR AMPLITUDE-FREQUENCY RESPONSE TO FILTER SIGNALS". En CAD/EDA/SIMULATION IN MODERN ELECTRONICS 2021. Bryansk State Technical University, 2021. http://dx.doi.org/10.30987/conferencearticle_61c997ef29ef52.74618218.

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The wavelet transform is the transmission of a signal through a bandpass filter. The design of wavelets with a rectangular amplitude-frequency response makes it possible to obtain almost ideal digital filters. The wavelet transform is calculated in the frequency domain using the fast Fourier transform.
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Freeman, Mark O., Ken A. Duell, Brett Bock y Adam S. Fedor. "Introduction to wavelets and considerations for optical implementation". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fa1.

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Wavelets have gained the attention of the signal processing community for their usefulness in analyzing nonstationary signals, for their mathematical elegance, and for their relative ease of computation. This paper is intended to introduce the audience to the basic principles of wavelet analysis and to consider where optical techniques can be applied advantageously. A signal is decomposed on a set of basis functions created by scaling and shifting a single fundamental wavelet. The space and frequency localization of the resulting wavelet transform, spanning the range from pure Nyquist sampling (no frequency localization) to Fourier transforms (no spatial localization), is determined by the choice of this fundamental wavelet. A common choice for the fundamental wavelet has compact support in the signal domain and bandpass-like behavior in the frequency domain. With this choice, rapidly varying information is well localized in the signal domain while slowly varying information is well localized in the frequency domain. We will discuss what constitutes an allowed fundamental wavelet, orthogonal and nonorthogonal wavelet bases, and the choice of sampling intervals in shift and scale. We will also discuss some of our theoretical results on filtering noise from nonstationary signals by using the wavelet transform for nonstationary spectrum estimation. In considering the use of optical techniques for wavelet computations, it is important to be aware of the digital competition. One reason for the popularity of wavelets is that O(N) algorithms exist for their digital computation. This is stiff competition for an optical system if the only advantage that can be claimed is speed. We will discuss the possible advantages of optical systems related to continuous rather than discretely sampled shift coordinates and the ease of implementing arbitrary scaling factors and nonseparable 2D wavelet functions. Finally, we will present an optical system for computing 2D wavelet transforms.
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Hopkins, Brad M. y Saied Taheri. "Broken Rail Prediction and Detection Using Wavelets and Artificial Neural Networks". En 2011 Joint Rail Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/jrc2011-56026.

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Current track health monitoring requires time consuming use of railway monitoring vehicles. This paper presents a rail defect detection and classification algorithm that could potentially be used with bogie side frame vertical acceleration data from a data acquisition system located onboard a train car during daily operation. The algorithm uses wavelets to process the vertical acceleration data and detect irregularities in the signal. Wavelets have proven themselves to be useful in event detection and other applications where localization is needed in both the time and frequency domains. Traditional signal processing methods may use the Fourier transform which is limited to localization only in the frequency domain. Wavelets provide a solution for recognizing rail defects and determining their location. The wavelet-processed data is fed into an artificial neural network for defect classification. Neural networks can be a powerful tool in pattern recognition and classification because of their ability to be taught. The network in this algorithm has been trained to recognize impending breaks and breaks in a rail from the original vertical acceleration signal and the first four scales of the wavelet transformed signal. This paper presents an offline analysis of a set of collected data using the proposed defect detection and classification algorithm.
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6

Newland, David E. "Practical Signal Analysis: Do Wavelets Make Any Difference?" En ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4135.

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Abstract Signal decomposition by time-frequency and time-scale mapping is an essential element of most diagnostic signal analysis. Is the wavelet method of decomposition any better than the short-time Fourier transform and Wigner-Ville methods? This paper explores the effectiveness of wavelets for diagnostic signal analysis. The author has found that harmonic wavelets are particularly suitable because of their simple structure in the frequency domain, but it is still difficult to produce high-definition time-frequency maps. New details of the theory of harmonic wavelet analysis are described which provide the basis for computational algorithms designed to improve map definition.
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Bonel-Cerdan, Jose I. y Jorgen L. Nikolajsen. "An Introduction to Harmonic Wavelet Analysis of Machine Vibrations". En ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-058.

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The Fast Wavelet Transform (FWT) is a powerful new tool which can be used for vibration analysis and condition monitoring of advanced rotating machinery. The main advantage of wavelet analysis for condition monitoring is that so-called wavelet maps can be produced showing three dimensional plots of amplitude versus frequency and time. This is in contrast to Fast Fourier Transform (FFT) analysis, in which the time domain of the signal is lost. The wavelet maps provide striking visual indications of tiny changes in machine behaviour which cannot be detected in a normal frequency spectrum. This improves the chances of averting catastrophic failures and expands the time window available to take corrective action. Additional advantages of wavelet analysis over FFT analysis include: (1) no requirements for periodicity of the signal, (2) extremely fast computation, (3) the location of patterns in the time domain and (4) an effective detection of high frequency details. Wavelet analyses of all types are available but user-friendly information is hard to come by and this has a detrimental effect on progress towards practical commercial applications. Thus, the main purpose of this paper is to provide a simple and clear introduction to wavelet analysis and its use in machine condition monitoring. The paper has been written for an audience having some familiarity with spectrum analysis but no prior knowledge of wavelets.
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8

Sheng, Yunglong, Danny Roberge, Taiwei Lu y Harold Szu. "Optical wavelet matched filters". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fn1.

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The wavelet transform decomposes a signal onto a set of basis wavelet functions that are dilated and shifted from the mother functions h(t), satisfying an admissible condition. This transform is compact in both time and frequency domains and is therefore efficient for time-dependent frequency analysis of the signal. We consider the wavelet transform as the correlations between the signal and a bank of wavelet filters, each having a fixed scale.1 Thus, the wavelet transform of a 1D signal is implemented in an optical correlator with multiple strip wavelet filters, and the wavelet transform of a 2D signal is implemented in a multichannel optical correlator. We make the matched filters recording the 4D wavelet transforms of a 2D input image for optical pattern recognition. With the isotropic Mexican-hat wavelets, the wavelet transform becomes the well known Laplacian-Gaussian operator for zero-crossing edge detection. However, we synthesize the filters by combining the wavelet transform filters and the conventional matched filters in the same Fourier plane for pattern recognition. The experimental results will be shown.
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9

Liaghat, M., A. Abdollahi, F. Daneshmand y T. Liaghat. "Wavelet Analysis of the Pressure Fluctuations of Bottom Outlet of Kamal-Saleh Dam". En ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12745.

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Non-stationary signals are frequently encountered in a variety of engineering fields. The inability of conventional Fourier analysis to preserve the time dependence and describe the evolutionary spectral characteristics of non-stationary processes requires tools which allow time and frequency localization beyond customary Fourier analysis. The spectral analysis of non-stationary signals cannot describe the local transient features due to averaging over the duration of the signal [1]. The Fourier Transform (FT) and the short time Fourier transform (STFT) have been often used to measure transient phenomena. These techniques yield good information on the frequency content of the transient, but the time at which a particular disturbance in the signal occurred is lost [2, 3]. Wavelets are relatively new analysis tools that are widely being used in signal analysis. In wavelet analysis, the transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave band of frequency. Wavelets do a very good job in detecting the time of the signal, but they give the frequency information in terms of frequency band regions or scales [4]. The main objective of this paper is to use the wavelet transform for analysis of the pressure fluctuations occurred in the bottom-outlet of Kamal-Saleh Dam. The “Kamalsaleh Dam” is located on the “Tire River” in Iran, near the Arak city. The Bottom Outlet of the dam is equipped with service gate and emergency gate. A hydraulic model test is conducted to investigate the dynamic behavior of the service gate of the outlet. The results of the calculations based on the wavelet transform is then compared with those obtained using the traditional Fast Fourier Transform.
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Zhang, Yan, Emmanuel G. Kanterakis, Al Katz y Jimmy M. Wang. "Optical wavelet processor for wavelets defined in the time domain". En SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, editado por Joseph L. Horner, Bahram Javidi, Stephen T. Kowel y William J. Miceli. SPIE, 1993. http://dx.doi.org/10.1117/12.163587.

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Informes sobre el tema "WAVELETE DOMAIN"

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Goda, Matthew E. Wavelet Domain Image Restoration and Super-Resolution. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2002. http://dx.doi.org/10.21236/ada405111.

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Weiss, Guido L. y M. V. Wickerhauser. Stable Feature Classification in the Wavelet Domain. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2000. http://dx.doi.org/10.21236/ada379900.

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Moore, Frank, Pat Marshall y Eric Balster. Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2004. http://dx.doi.org/10.21236/ada427113.

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Hippenstiel, R. Signal to Noise Ratio Improvement Using Wavelet and Frequency Domain Based Processing. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2002. http://dx.doi.org/10.21236/ada404025.

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An, M., R. Tolimieri, J. Weiss y J. Byrnes. Thermal Analysis of Multichip Modules Using Domain Decomposition and Wavelet-Capacitance Matrix. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1996. http://dx.doi.org/10.21236/ada329533.

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Rane, Shantanu D., Jeremiah Remus y Guillermo Sapiro. Wavelet-Domain Reconstruction of Lost Blocks in Wireless Image Transmission and Packet-Switched Networks. Fort Belvoir, VA: Defense Technical Information Center, enero de 2005. http://dx.doi.org/10.21236/ada437341.

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Nafi Toksoez, M. Characterization of an Explosion Source in a Complex Medium by Modeling and Wavelet Domain Inversion. Fort Belvoir, VA: Defense Technical Information Center, junio de 2006. http://dx.doi.org/10.21236/ada455323.

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Andrian, Jean. Fratricide Avoidance Using Transform Domain Techniques: A New Spectral Estimation Method Based on the Evolutionary Wavelet Spectrum Concept. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2006. http://dx.doi.org/10.21236/ada448936.

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Anderson, Gerald L. y Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, diciembre de 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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