Gotowe bibliografie tematyczne / Wavelet transforms

Gotowa bibliografia na temat „Wavelet transforms”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 7 września 2023

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Artykuły w czasopismach na temat "Wavelet transforms"

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Romanchak, V. M. "Local transformations with a singular wavelet". Informatics 17, nr 1 (29.03.2020): 39–46. http://dx.doi.org/10.37661/1816-0301-2020-17-1-39-46.

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The paper considers a local wavelet transform with a singular basis wavelet. The problem of nonparametric approximation of a function is solved by the use of the sequence of local wavelet transforms. Traditionally believed that the wavelet should have an average equal to zero. Earlier, the author considered singular wavelets when the average value is not equal to zero. As an example, the delta-shaped functions, participated in the estimates of Parzen – Rosenblatt and Nadara – Watson, were used as a wavelet. Previously, a sequence of wavelet transforms for the entire numerical axis and finite interval was constructed for singular wavelets. The paper proposes a sequence of local wavelet transforms, a local wavelet transform is defined, the theorems that formulate the properties of a local wavelet transform are proved. To confirm the effectiveness of the algorithm an example of approximating the function by use of the sum of discrete local wavelet transforms is given.
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Zeelan Basha, CMAK, K. M. Sricharan, Ch Krishna Dheeraj i R. Ramya Sri. "A Study on Wavelet Transform Using Image Analysis". International Journal of Engineering & Technology 7, nr 2.32 (31.05.2018): 94. http://dx.doi.org/10.14419/ijet.v7i2.32.13535.

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The wavelet transforms have been in use for variety of applications. It is widely being used in signal analysis and image analysis. There have been lot of wavelet transforms for compression, decomposition and reconstruction of images. Out of many transforms Haar wavelet transform is the most computationally feasible wavelet transform to implement. The wave analysis technique has an understandable impact on the removal of noise within the signal. The paper outlines the principles and performance of wavelets in image analysis. Compression performance and decomposition of images into different layers have been discussed in this paper. We used Haar distinct wavelet remodel (HDWT) to compress the image. Simulation of wavelet transform was done in MATLAB. Simulation results are conferred for the compression with Haar rippling with totally different level of decomposition. Energy retention and PSNR values are calculated for the wavelets. Result conjointly reveals that the extent of decomposition will increase beholding of the photographs goes on decreasing however the extent of compression is incredibly high. Experimental results demonstrate the effectiveness of the Haar wavelet transform in energy retention in comparison to other wavelet transforms.
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TODA, HIROSHI, ZHONG ZHANG i TAKASHI IMAMURA. "PERFECT-TRANSLATION-INVARIANT CUSTOMIZABLE COMPLEX DISCRETE WAVELET TRANSFORM". International Journal of Wavelets, Multiresolution and Information Processing 11, nr 04 (lipiec 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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Jansen, Maarten. "Non-equispaced B-spline wavelets". International Journal of Wavelets, Multiresolution and Information Processing 14, nr 06 (listopad 2016): 1650056. http://dx.doi.org/10.1142/s0219691316500569.

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This paper has three main contributions. The first is the construction of wavelet transforms from B-spline scaling functions defined on a grid of non-equispaced knots. The new construction extends the equispaced, biorthogonal, compactly supported Cohen–Daubechies–Feauveau wavelets. The new construction is based on the factorization of wavelet transforms into lifting steps. The second and third contributions are new insights on how to use these and other wavelets in statistical applications. The second contribution is related to the bias of a wavelet representation. It is investigated how the fine scaling coefficients should be derived from the observations. In the context of equispaced data, it is common practice to simply take the observations as fine scale coefficients. It is argued in this paper that this is not acceptable for non-interpolating wavelets on non-equidistant data. Finally, the third contribution is the study of the variance in a non-orthogonal wavelet transform in a new framework, replacing the numerical condition as a measure for non-orthogonality. By controlling the variances of the reconstruction from the wavelet coefficients, the new framework allows us to design wavelet transforms on irregular point sets with a focus on their use for smoothing or other applications in statistics.
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Pathak, R. S., i S. K. Singh. "The wavelet transform on spaces of type S". Proceedings of the Royal Society of Edinburgh: Section A Mathematics 136, nr 4 (sierpień 2006): 837–50. http://dx.doi.org/10.1017/s0308210500004753.

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The continuous wavelet transform is studied on certain Gel'fand–Shilov spaces of type S. It is shown that, for wavelets belonging to the one type of S-space defined on R, the wavelet transform is a continuous linear map of the other type of the S-space into a space of the same type (latter type) defined on R × R+. The wavelet transforms of certain ultradifferentiable functions are also investigated.
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Guido, Rodrigo Capobianco, Fernando Pedroso, André Furlan, Rodrigo Colnago Contreras, Luiz Gustavo Caobianco i Jogi Suda Neto. "CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms". International Journal of Wavelets, Multiresolution and Information Processing 18, nr 06 (28.08.2020): 2030001. http://dx.doi.org/10.1142/s0219691320300017.

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Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.
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Abdullah, Shahrum, S. N. Sahadan, Mohd Zaki Nuawi i Zulkifli Mohd Nopiah. "Fatigue Data Analysis Using Continuous Wavelet Transform and Discrete Wavelet Transform". Key Engineering Materials 462-463 (styczeń 2011): 461–66. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.461.

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The wavelet transform is well known for its ability in vibration analysis in fault detection. This paper presents the ability of wavelet transform in fatigue data analysis starts from high amplitude events detection and it is then followed by fatigue data extraction based on wavelet coefficients. Since the wavelet transform has two main categories, i.e. the continuous wavelet transforms (CWT) and the discrete wavelet transform (DWT), the comparison study were carried out in order to investigate performance of both wavelet for fatigue data analysis. CWT represents by the Morlet wavelet while DWT with the form of the 4th Order Daubechies wavelet (Db4) was also used for the analysis. An analysis begins with coefficients plot using the time-scale representation that associated to energy coefficients plot for the input value in fatigue data extraction. Ten extraction levels were used and all levels gave the damage difference, (%∆D) less than 10% with respect to original signal. From the study, both wavelet transforms gave almost similar ability in editing fatigue data but the Morlet wavelet provided faster analysis time compared to the Db4 wavelet. In comparison to have the value of different at 5%, the Morlet wavelet achieved at L= 5 while the Db4 wavelet at L=7. Even though it gave slower analysis time, both wavelets can be used in fatigue data editing but at different time consuming.
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Kekre, H. B., Tanuja Sarode i Shachi Natu. "Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, nr 3 (15.07.2013): 1139–52. http://dx.doi.org/10.24297/ijct.v9i3.3340.

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This paper proposes a watermarking technique using different orthogonal wavelet transforms like Hartley wavelet, Kekrewavelet, Slant wavelet and Real Fourier wavelet transform generated from corresponding orthogonal transform. Theseorthogonal wavelet transforms have been generated using different sizes of component orthogonal transform matrices.For example 256*256 size orthogonal wavelet transform can be generated using 128*128 and 2*2 size componentorthogonal transform. It can also be generated using 64*64 and 4*4, 32*32 and 8*8, 16*16 and 16*16 size componentorthogonal transform matrices. In this paper the focus is to compare the performance of above mentioned transformsgenerated using 128*128 and 2*2 size component orthogonal transform and 64*64 and 4*4 size component orthogonaltransform in digital image watermarking. The other two combinations are not considered as their performance iscomparatively not as good. Comparison shows that wavelet transforms generated using (128,2) combination of orthogonal transform give better performances than wavelet transforms generated using (64,4) combination of orthogonaltransformfor contrast stretching, cropping, Gaussian noise, histogram equalization and resizing attacks. Real Fourierwavelet and Slant wavelet prove to be better for histogram equalization and resizing attack respectively than DCT waveletand Walsh wavelet based watermarking presented in previous work.
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Taha, Saleem, i Walid Mahmood. "New techniques for Daubechies wavelets and multiwavelets implementation using quantum computing". Facta universitatis - series: Electronics and Energetics 26, nr 2 (2013): 145–56. http://dx.doi.org/10.2298/fuee1302145t.

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In this paper, new techniques to implement the Daubechies wavelets and multiwavelets are presented using quantum computing synthesis structures. Also, a new quantum implementation of inverse Daubechies multiwavelet transform is proposed. The permutation matrices, particular unitary matrices, play a pivotal role. The particular set of permutation matrices arising in quantum wavelet and multiwavelet transforms is considered, and efficient quantum circuits that implement them are developed. This allows the design of efficient and complete quantum circuits for the quantum wavelet and multiwavelet transforms.
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TODA, HIROSHI, ZHONG ZHANG i TAKASHI IMAMURA. "THE DESIGN OF COMPLEX WAVELET PACKET TRANSFORMS BASED ON PERFECT TRANSLATION INVARIANCE THEOREMS". International Journal of Wavelets, Multiresolution and Information Processing 08, nr 04 (lipiec 2010): 537–58. http://dx.doi.org/10.1142/s0219691310003638.

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The useful theorems for achieving perfect translation invariance have already been proved, and based on these theorems, dual-tree complex discrete wavelet transforms with perfect translation invariance have been proposed. However, due to the complication of frequency divisions with wavelet packets, it is difficult to design complex wavelet packet transforms with perfect translation invariance. In this paper, based on the aforementioned theorems, novel complex wavelet packet transforms are designed to achieve perfect translation invariance. These complex wavelet packet transforms are based on the Meyer wavelet, which has the important characteristic of possessing a wide range of shapes. In this paper, two types of complex wavelet packet transforms are designed with the optimized Meyer wavelet. One of them is based on a single Meyer wavelet and the other is based on a number of different shapes of the Meyer wavelets to create good localization of wavelet packets.
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Rozprawy doktorskie na temat "Wavelet transforms"

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Shi, Fangmin. "Wavelet transforms for stereo imaging". Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/wavelet-transforms-for-stereo-imaging(65abb68f-e30b-4367-a3a8-b7b3df85f566).html.

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Stereo vision is a means of obtaining three-dimensional information by considering the same scene from two different positions. Stereo correspondence has long been and will continue to be the active research topic in computer vision. The requirement of dense disparity map output is great demand motivated by modern applications of stereo such as three-dimensional high-resolution object reconstruction and view synthesis, which require disparity estimates in all image regions. Stereo correspondence algorithms usually require significant computation. The challenges are computational economy, accuracy and robustness. While a large number of algorithms for stereo matching have been developed, there still leaves the space for improvement especially when a new mathematical tool such as wavelet analysis becomes mature. The aim of the thesis is to investigate the stereo matching approach using wavelet transform with a view to producing efficient and dense disparity map outputs. After the shift invariance property of various wavelet transforms is identified, the main contributions of the thesis are made in developing and evaluating two wavelet approaches (the dyadic wavelet transform and complex wavelet transform) for solving the standard correspondence problem. This comprises an analysis of the applicability of dyadic wavelet transform to disparity map computation, the definition of a waveletbased similarity measure for matching, the combination of matching results from different scales based on the detectable minimum disparity at each scale and the application of complex wavelet transform to stereo matching. The matching method using the dyadic wavelet transform is through SSD correlation comparison and is in particular detailed. A new measure using wavelet coefficients is defined for similarity comparison. The approach applying a dual tree of complex wavelet transform to stereo matching is formulated through phase information. A multiscale matching scheme is applied for both the matching methods. Imaging testing has been made with various synthesised and real image pairs. Experimental results with a variety of stereo image pairs exhibit a good agreement with ground truth data, where available, and are qualitatively similar to published results for other stereo matching approaches. Comparative results show that the dyadic wavelet transform-based matching method is superior in most cases to the other approaches considered.
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Parker, Kristen Michelle. "Watermarking with wavelet transforms". Master's thesis, Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-11062007-153859.

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Rivera, Vega Nestor. "Reservoir characterization using wavelet transforms". Texas A&M University, 2003. http://hdl.handle.net/1969.1/482.

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Automated detection of geological boundaries and determination of cyclic events controlling deposition can facilitate stratigraphic analysis and reservoir characterization. This study applies the wavelet transformation, a recent advance in signal analysis techniques, to interpret cyclicity, determine its controlling factors, and detect zone boundaries. We tested the cyclostratigraphic assessments using well log and core data from a well in a fluvio-eolian sequence in the Ormskirk Sandstone, Irish Sea. The boundary detection technique was tested using log data from 10 wells in the Apiay field, Colombia. We processed the wavelet coefficients for each zone of the Ormskirk Formation and determined the wavelengths of the strongest cyclicities. Comparing these periodicities with Milankovitch cycles, we found a strong correspondence of the two. This suggests that climate exercised an important control on depositional cyclicity, as had been concluded in previous studies of the Ormskirk Sandstone. The wavelet coefficients from the log data in the Apiay field were combined to form features. These vectors were used in conjunction with pattern recognition techniques to perform detection in 7 boundaries. For the upper two units, the boundary was detected within 10 feet of their actual depth, in 90% of the wells. The mean detection performance in the Apiay field is 50%. We compared our method with other traditional techniques which do not focus on selecting optimal features for boundary identification. Those methods resulted in detection performances of 40% for the uppermost boundary, which lag behind the 90% performance of our method. Automated determination of geologic boundaries will expedite studies, and knowledge of the controlling deposition factors will enhance stratigraphic and reservoir characterization models. We expect that automated boundary detection and cyclicity analysis will prove to be valuable and time-saving methods for establishing correlations and their uncertainties in many types of oil and gas reservoirs, thus facilitating reservoir exploration and management.
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Silva, Eduardo Antonio Barros da. "Wavelet transforms for image coding". Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282495.

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Choe, Gwangwoo. "Merged arithmetic for wavelet transforms /". Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004235.

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Boettcher, Joseph Bradley. "VIDEO CODING WITH 3D WAVELET TRANSFORMS". MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11082007-072709/.

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Video coding systems based on 3D wavelet transforms offer several advantages over traditional hybrid video coders. This thesis proposes two 3D wavelet-based video-coding approaches. In the first approach, motion compensation with redundant-wavelet multihypothesis, in which multiple predictions that are diverse in transform phase contribute to a single motion estimate, is deployed into the fully scalable MC-EZBC video coder. The bidirectional motion-compensated temporal-filtering process of MC-EZBC is adapted to the redundant-wavelet domain, wherein transform redundancy is exploited to generate a phase-diverse multihypothesis prediction of the true temporal filtering. In the second approach, a video coder is proposed that does not perform motion compensation explicitly, instead relying on the motion-selective characteristics of the 3D dual-tree discrete wavelet transform to isolate moving features. The transform coefficients are coded with binary set-partitioning using k-d trees in an algorithm that exploits within-subband spatiotemporal coherency as well as cross-subband correlation to achieve efficient coding.
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Masud, Shahid. "VLSI systems for discrete wavelet transforms". Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300782.

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Lucrecio, Armando. "ISAR imaging using Fourier and wavelet transforms". Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Dec%5FLucrecio.pdf.

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Thesis (M.S. in Physics)--Naval Postgraduate School, December 2007.
Thesis Advisor(s): Borden, Brett ; Cristi, Roberto. "December 2007." Description based on title screen as viewed on January 23, 2008 Includes bibliographical references (p. 61-62). Also available in print.
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Legaspi, Joey E. "One and two dimensional discrete wavelet transforms". Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23739.

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Ferreira, Milton dos Santos. "Continuous wavelet transforms on the unit sphere". Doctoral thesis, Universidade de Aveiro, 2008. http://hdl.handle.net/10773/2950.

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Książki na temat "Wavelet transforms"

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Akansu, Ali N., i Mark J. T. Smith, red. Subband and Wavelet Transforms. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0483-8.

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A, Gopinath Ramesh, i Guo Haitao, red. Introduction to wavelets and wavelet transforms: A primer. Upper Saddle River, N.J: Prentice Hall, 1998.

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Wong, M. W. Wavelet Transforms and Localization Operators. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8217-0.

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Debnath, Lokenath. Wavelet Transforms and Their Applications. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0097-0.

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Debnath, Lokenath, i Firdous Ahmad Shah. Wavelet Transforms and Their Applications. Boston, MA: Birkhäuser Boston, 2015. http://dx.doi.org/10.1007/978-0-8176-8418-1.

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Debnath, Lokenath, i Firdous A. Shah. Lecture Notes on Wavelet Transforms. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59433-0.

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Wavelet transforms and localization operators. Basel: Birkhäuser Verlag, 2002.

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Debnath, Lokenath. Wavelet Transforms and Their Applications. Boston, MA: Birkhäuser Boston, 2002.

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Song, Goh Say, Ron Amos i Shen Zuowei, red. Gabor and wavelet frames. New Jersey: World Scientific, 2007.

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Ingrid, Daubechies, Mallat Stephane i Willsky Alan S, red. Wavelet transforms and multiresolution signal analysis. New York: IEEE, 1992.

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Części książek na temat "Wavelet transforms"

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Akujuobi, Cajetan M. "Wavelet Transforms". W Wavelets and Wavelet Transform Systems and Their Applications, 61–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87528-2_4.

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Shah, Firdous A., i Azhar Y. Tantary. "The Fourier Transforms". W Wavelet Transforms, 1–118. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-1.

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Shah, Firdous A., i Azhar Y. Tantary. "The Wavelet Transforms and Kin". W Wavelet Transforms, 193–324. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-3.

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Shah, Firdous A., i Azhar Y. Tantary. "The Intertwining of Wavelet Transforms". W Wavelet Transforms, 325–412. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-4.

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Shah, Firdous A., i Azhar Y. Tantary. "The Windowed Fourier Transforms". W Wavelet Transforms, 119–92. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-2.

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Shah, Firdous A., i Azhar Y. Tantary. "The Wavelet Transforms and Kith". W Wavelet Transforms, 413–56. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-5.

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Mourad, Talbi. "Wavelets and Wavelet Transforms". W ECG Denoising Based on Total Variation Denoising and Wavelets, 1–18. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9_1.

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Farouk, Mohamed Hesham. "Wavelets, Wavelet Filters, and Wavelet Transforms". W SpringerBriefs in Electrical and Computer Engineering, 11–19. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02732-6_3.

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Farouk, Mohamed Hesham. "Wavelets, Wavelet Filters, and Wavelet Transforms". W SpringerBriefs in Electrical and Computer Engineering, 11–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69002-5_3.

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Wong, M. W. "Wavelet Transforms". W Wavelet Transforms and Localization Operators, 48–50. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8217-0_7.

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Streszczenia konferencji na temat "Wavelet transforms"

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Sheng, Yunglong, Danny Roberge, Taiwei Lu i Harold Szu. "Optical wavelet matched filters". W 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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Thepade, Sudeep D., i Smita S. Chavan. "Vigorous image steganography with transforms, wavelet transforms and hybrid wavelet transforms". W 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030361.

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Srinivasan, R. S., i Kristin L. Wood. "Wavelet Transforms in Fractal-Based Form Tolerancing". W ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0007.

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Abstract Tolerancing is a crucial problem for mechanical designers, as it has quality and cost implications on product design. Research in tolerancing has addressed specific areas of the problem. Building upon previous research, a unified approach for geometric tolerancing with fractal-based parameters has been recently proposed. This paper explores an alternative error profile analysis and synthesis method, based on wavelets, that maintains and extends the use of fractals for surface error abstraction. An overview of the theory of wavelets is provided, and the link between fractals and wavelets is established. Experimental data are used to illustrate the application of wavelet theory to surface profile reconstruction and synthesis. The synthesis methods are then implemented in the design of ball-bearing elements, demonstrating the utility of fractal-based tolerancing. Plans for further study and implementation conclude the paper.
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Katz, A., E. G. Kanterakis, Y. Zhang, X. J. Lu i N. P. Caviris. "A joint transform correlator utilizing the wavelet transform". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fn4.

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Wavelet transforms can be used to decompose a signal (image) into orthogonal components. When a small set of these components is used to form a wavelet transform sub-image, only specific characteristics of the image are retained. It is the identification of these characteristics that can be used to perform pattern recognition. A joint transform correlation system utilizing wavelet transforms is used to perform pattern recognition. An optical implementation of this system is described. Problems with respect to scale, rotation and translation are addressed and experimental results are presented.
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Thepade, Sudeep D., i Pooja Bidwai. "Iris recognition using fractional coefficients of transforms, Wavelet Transforms and Hybrid Wavelet Transforms". W 2013 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2013. http://dx.doi.org/10.1109/iccccm.2013.6648921.

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Freeman, Mark O., Ken A. Duell, Brett Bock i Adam S. Fedor. "Introduction to wavelets and considerations for optical implementation". W 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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Li, Yao. "Optics for wavelet-based processing". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fa2.

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Recent developments in signal processing, vision, and image understanding reveal that a proper signal or image decomposition before the actual processing may provide enormously useful information about the signal or image. The wavelet transform a particularly useful model, provides a multi resolution linear signal/image time-frequency or space-frequency decomposition tool. Successful applications of wavelet transforms to solve difficult signal or image analysis and synthesis problems have been widely reported. Digital implementations of these transforms are computationally intensive both because of the nature of the coordinate doubling of the wavelet transform and because of the large quantity of convolution/correlation operations that accompany them. Optics with its inherent parallel processing capability, has been used to obtain many useful linear signal/image transformations and will certainly have some roles in wavelet-based processing. This survey talk is intended to outline the suitability of using existing optical processing techniques for wavelet-based signal/image analysis and synthesis. A brief summary of various proposed optical wavelet transform approaches together with their advantages and constraints will be presented. Possible future directions in this new research field will also be discussed.
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Chen, Da Jun, i Wei Ji Wang. "Pattern Changes of Time-Shifted Vibration Signals on Wavelet Time-Scale Maps". W ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0381.

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Abstract As a multi-resolution signal decomposition and analysis technique, the wavelet transforms have been already introduced to vibration signal processing. In this paper, a comparison on the time-scale map analysis is made between the discrete and the continuous wavelet transform. The orthogonal wavelet transform decomposes the vibration signal onto a series of orthogonal wavelet functions and the number of wavelets on one wavelet level is different from those on the other levels. Since the grids are unevenly distributed on the time-scale map, it is shown that a representation pattern of a vibration component on the map may be significantly altered or even be broken down into pieces when the signal has a shift along the time axis. On contrary, there is no such uneven distribution of grids on the continuous wavelet time-scale map, so that the representation pattern of a vibration signal component will not change its shape when the signal component shifts along the time axis. Therefore, the patterns in the continuous wavelet time-scale map are more easily recognised by human visual inspection or computerised automatic diagnosis systems. Using a Gaussian enveloped oscillation wavelet, the wavelet transform is capable of retaining the frequency meaning used in the spectral analysis, while making the interpretation of patterns on the time-scale maps easier.
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Dawkins, Jeremy J., David M. Bevly i Robert L. Jackson. "Multiscale Terrain Characterization Using Fourier and Wavelet Transforms for Unmanned Ground Vehicles". W ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2718.

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This paper investigates the use of the Fourier transform and Wavelet transform as methods to supplement the more common root mean squared elevation and power spectral density methods of terrain characterization. Two dimensional terrain profiles were generated using the Weierstrass-Mandelbrot fractal equation. The Fourier and Wavelet transforms were used to decompose these terrains into a parameter set. A two degree of freedom quarter car model was used to evaluate the vehicle response before and after the terrain characterization. It was determined that the Fourier transform can be used to reduce the profile into the key frequency components. The Wavelet transform can effectively detect discontinuities of the profile and changes in the roughness of the profile. These two techniques can be added to current methods to yield a more robust terrain characterization.
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Lane, F. D., i D. J. Foster. "Introduction to wavelet transforms". W SEG Technical Program Expanded Abstracts 1996. Society of Exploration Geophysicists, 1996. http://dx.doi.org/10.1190/1.1826574.

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Raporty organizacyjne na temat "Wavelet transforms"

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Li, Shaomeng, i Christopher Meyer Sewell. Wavelet Transforms using VTK-m. Office of Scientific and Technical Information (OSTI), wrzesień 2016. http://dx.doi.org/10.2172/1329546.

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Gyaourova, A., C. Kamath i I. K. Fodor. Undecimated Wavelet Transforms for Image De-noising. Office of Scientific and Technical Information (OSTI), listopad 2002. http://dx.doi.org/10.2172/15002085.

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Bhattacharya, Prabir. Automatic Target Recognition, Wavelet Transforms and Stereo Matching. Fort Belvoir, VA: Defense Technical Information Center, marzec 2001. http://dx.doi.org/10.21236/ada399734.

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Tang, Xiaoou, i W. K. Stewart. Texture Classification Using Wavelet Packet and Fourier Transforms. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1995. http://dx.doi.org/10.21236/ada324161.

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Zweig, G. Wavelet transforms as solutions of partial differential equations. Office of Scientific and Technical Information (OSTI), październik 1997. http://dx.doi.org/10.2172/534535.

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Hippenstiel, Ralph D., Monique P. Fargues, Nabil H. Khalil i Howard F. Overdyk. Processing of Second Order Statistics via Wavelet Transforms. Fort Belvoir, VA: Defense Technical Information Center, luty 1998. http://dx.doi.org/10.21236/ada339331.

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Chou, K. C., S. A. Golden i A. S. Willsky. Multiresolution Stochastic Models, Data Fusion, and Wavelet Transforms. Fort Belvoir, VA: Defense Technical Information Center, maj 1992. http://dx.doi.org/10.21236/ada459326.

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Lin, Jyhfong, Yagyensh Pati, Thomas Edwards i Shihab Shamma. Analog VLSI Implementations of Auditory Wavelet Transforms Using Switched-Capacitor Circuits. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1992. http://dx.doi.org/10.21236/ada455019.

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Dew, Eric, i Robert J. Lipshutz. Low Frequency Active Signal Detection Methodology and Simulation Employing Discrete Wavelet Transforms. Fort Belvoir, VA: Defense Technical Information Center, listopad 1992. http://dx.doi.org/10.21236/ada260007.

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Shensa, M. J. Discrete Wavelet Transforms: The Relationship of the a Trous and Mallat Algorithms. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1991. http://dx.doi.org/10.21236/ada244882.

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