Relevant bibliographies by topics / Wavelet transforms

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

Academic literature on the topic 'Wavelet transforms'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Wavelet transforms"

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Romanchak, V. M. "Local transformations with a singular wavelet." Informatics 17, no. 1 (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 i
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Zeelan Basha, CMAK, K. M. Sricharan, Ch Krishna Dheeraj, and R. Ramya Sri. "A Study on Wavelet Transform Using Image Analysis." International Journal of Engineering & Technology 7, no. 2.32 (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 la
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TODA, HIROSHI, ZHONG ZHANG, and TAKASHI IMAMURA. "PERFECT-TRANSLATION-INVARIANT CUSTOMIZABLE COMPLEX DISCRETE WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 04 (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
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Jansen, Maarten. "Non-equispaced B-spline wavelets." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 06 (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 f
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Pathak, R. S., and S. K. Singh. "The wavelet transform on spaces of type S." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 136, no. 4 (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, and 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, no. 06 (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
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Abdullah, Shahrum, S. N. Sahadan, Mohd Zaki Nuawi, and Zulkifli Mohd Nopiah. "Fatigue Data Analysis Using Continuous Wavelet Transform and Discrete Wavelet Transform." Key Engineering Materials 462-463 (January 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 DW
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Kekre, H. B., Tanuja Sarode, and Shachi Natu. "Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 3 (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
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TODA, HIROSHI, ZHONG ZHANG, and TAKASHI IMAMURA. "THE DESIGN OF COMPLEX WAVELET PACKET TRANSFORMS BASED ON PERFECT TRANSLATION INVARIANCE THEOREMS." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 04 (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 t
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Taha, Saleem, and Walid Mahmood. "New techniques for Daubechies wavelets and multiwavelets implementation using quantum computing." Facta universitatis - series: Electronics and Energetics 26, no. 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
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Dissertations / Theses on the topic "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, accur
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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,
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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
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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.<br>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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Books on the topic "Wavelet transforms"

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

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A, Gopinath Ramesh, and Guo Haitao, eds. Introduction to wavelets and wavelet transforms: A primer. Prentice Hall, 1998.

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Wong, M. W. Wavelet Transforms and Localization Operators. 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. Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0097-0.

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

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

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

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Song, Goh Say, Ron Amos, and Shen Zuowei, eds. Gabor and wavelet frames. World Scientific, 2007.

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Ingrid, Daubechies, Mallat Stephane, and Willsky Alan S, eds. Wavelet transforms and multiresolution signal analysis. IEEE, 1992.

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Debnath, Lokenath, ed. Wavelet Transforms and Time-Frequency Signal Analysis. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0137-3.

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Book chapters on the topic "Wavelet transforms"

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

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

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Shah, Firdous A., and Azhar Y. Tantary. "The Wavelet Transforms and Kin." In Wavelet Transforms. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-3.

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Shah, Firdous A., and Azhar Y. Tantary. "The Intertwining of Wavelet Transforms." In Wavelet Transforms. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-4.

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Shah, Firdous A., and Azhar Y. Tantary. "The Windowed Fourier Transforms." In Wavelet Transforms. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-2.

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Shah, Firdous A., and Azhar Y. Tantary. "The Wavelet Transforms and Kith." In Wavelet Transforms. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175766-5.

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Mourad, Talbi. "Wavelets and Wavelet Transforms." In ECG Denoising Based on Total Variation Denoising and Wavelets. 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." In SpringerBriefs in Electrical and Computer Engineering. 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." In SpringerBriefs in Electrical and Computer Engineering. 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." In Wavelet Transforms and Localization Operators. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8217-0_7.

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Conference papers on the topic "Wavelet transforms"

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Devarapalli, Sri Harshavardhan Reddy, Harshdeep Jadhav, and Jayasri Dontabhaktuni. "Image compression using quantum wavelet transforms." In Quantum Computing, Communication, and Simulation V, edited by Philip R. Hemmer and Alan L. Migdall. SPIE, 2025. https://doi.org/10.1117/12.3056452.

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

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Srinivasan, R. S., and Kristin L. Wood. "Wavelet Transforms in Fractal-Based Form Tolerancing." In 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 wavele
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Salama, Rana, Abdou Youssef, and Mona Diab. "Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding." In 5th International Conference on Advanced Natural Language Processing. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141006.

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Wavelets have emerged as a cutting edge technology in a number of fields. Concrete results of their application in Image and Signal processing suggest that wavelets can be effectively applied to Natural Language Processing (NLP) tasks that capture a variety of linguistic properties. In this paper, we leverage the power of applying Discrete Wavelet Transforms (DWT) to word and sentence embeddings. We first evaluate, intrinsically and extrinsically, how wavelets can effectively be used to consolidate important information in a word vector while reducing its dimensionality. We further combine DWT
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Katz, A., E. G. Kanterakis, Y. Zhang, X. J. Lu, and N. P. Caviris. "A joint transform correlator utilizing the wavelet transform." In OSA Annual Meeting. 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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Freeman, Mark O., Ken A. Duell, Brett Bock, and Adam S. Fedor. "Introduction to wavelets and considerations for optical implementation." In OSA Annual Meeting. 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
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Thepade, Sudeep D., and Pooja Bidwai. "Iris recognition using fractional coefficients of transforms, Wavelet Transforms and Hybrid Wavelet Transforms." In 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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Li, Yao. "Optics for wavelet-based processing." In OSA Annual Meeting. 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 becau
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Coelho, Eduardo Rittner, Ana Clara C. Silveira, Tarik P. e. Sá, et al. "Hyperspectral and Multispectral Image Fusion Using 3D Wavelet Transforms." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/sibgrapi.est.2024.31665.

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The fusion of multispectral (MSI) and hyperspectral (HSI) images is a crucial technique in various fields such as remote sensing, medical imaging, and agricultural monitoring. MSI captures light across several specific spectral bands, while HSI provides detailed spectral information across contiguous bands. Combining these two types of images leverages the high spatial resolution of MSI and the rich spectral content of HSI, creating a single, high-resolution image that is both spatially and spectrally informative. Traditional wavelet based fusion methods often employ a single wavelet across al
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Reports on the topic "Wavelet transforms"

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

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

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

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Tang, Xiaoou, and W. K. Stewart. Texture Classification Using Wavelet Packet and Fourier Transforms. Defense Technical Information Center, 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), 1997. http://dx.doi.org/10.2172/534535.

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

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

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

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Dew, Eric, and Robert J. Lipshutz. Low Frequency Active Signal Detection Methodology and Simulation Employing Discrete Wavelet Transforms. Defense Technical Information Center, 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. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada244882.

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