Relevant bibliographies by topics / Wavelet transform

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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 transform'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Wavelet transform"

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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 (July 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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Romanchak, V. M. "Local transformations with a singular wavelet." Informatics 17, no. 1 (March 29, 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, and R. Ramya Sri. "A Study on Wavelet Transform Using Image Analysis." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 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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Willmore, Ben, Ryan J. Prenger, Michael C. K. Wu, and Jack L. Gallant. "The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform." Neural Computation 20, no. 6 (June 2008): 1537–64. http://dx.doi.org/10.1162/neco.2007.05-07-513.

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We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.
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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 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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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 (August 28, 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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Abuhamdia, Tariq, Saied Taheri, and John Burns. "Laplace wavelet transform theory and applications." Journal of Vibration and Control 24, no. 9 (May 11, 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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Hamdi, Med. "A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images." Image Processing & Communications 16, no. 3-4 (January 1, 2011): 13–20. http://dx.doi.org/10.2478/v10248-012-0007-1.

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical ImagesA special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio
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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 (August 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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Mandal, U. K., Sandeep Verma, and Akhilesh Prasad. "Composition of wavelet transforms and wave packet transform involving Kontorovich-Lebedev transform." Filomat 35, no. 1 (2021): 47–60. http://dx.doi.org/10.2298/fil2101047m.

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The main objective of this paper is to study the composition of continuous Kontorovich-Lebedev wavelet transform (KL-wavelet transform) and wave packet transform (WPT) based on the Kontorovich-Lebedev transform (KL-transform). Estimates for KL-wavelet and KL-wavelet transform are obtained, and Plancherel?s relation for composition of KL-wavelet transform and WPT-transform are derived. Reconstruction formula for WPT associated to KL-transform is also deduced and at the end Calderon?s formula related to KL-transform using its convolution property is obtained.
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Dissertations / Theses on the topic "Wavelet transform"

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Anton, Wirén. "The Discrete Wavelet Transform." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55063.

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In this thesis we will explore the theory behind wavelets. The main focus is on the discrete wavelet transform, although to reach this goal we will also introduce the discrete Fourier transform as it allow us to derive important properties related to wavelet theory, such as the multiresolution analysis. Based on the multiresolution it will be shown how the discrete wavelet transform can be formulated and show how it can be expressed in terms of a matrix. In later chapters we will see how the discrete wavelet transform can be generalized into two dimensions, and discover how it can be used in image processing.
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Navarro, Jaime. "The Continuous Wavelet Transform and the Wave Front Set." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc277762/.

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In this paper I formulate an explicit wavelet transform that, applied to any distribution in S^1(R^2), yields a function on phase space whose high-frequency singularities coincide precisely with the wave front set of the distribution. This characterizes the wave front set of a distribution in terms of the singularities of its wavelet transform with respect to a suitably chosen basic wavelet.
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Huang, Wensheng. "Wavelet Transform Adaptive Signal Detection." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19991104-151423.

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Wavelet Transform Adaptive Signal Detection is a signal detection method that uses the Wavelet Transform Adaptive Filter (WTAF). The WTAF is the application of adaptive filtering on the subband signals obtained by wavelet decomposition and reconstruction. The WTAF is an adaptive filtering technique that leads to good convergence and low computational complexity. It can effectively adapt to non-stationary signals, and thus could find practical use for transient signals. Different architectures for implementing the WTAF were proposed and studied in this dissertation. In terms of the type of the wavelet transform being used, we presented the DWT based WTAF and the wavelet tree based WTAF. In terms of the position of the adaptive filter in the signal paths of the system, we presented the Before-Reconstruction WTAF, in which the adaptive filter is placed before the reconstruction filter; and the After-Reconstruction WTAF, in which the adaptive filter is placed after the reconstruction filter. This could also be considered as implementing the adaptive filtering in different domains, with the Before-Reconstruction structure corresponding to adaptive filtering in the scale-domain, and the After-Reconstruction structure corresponding to adaptive filtering in the time-domain. In terms of the type of the error signal used in the WTAF, we presented the output error based WTAF and the subband error based WTAF. In the output error based WTAF, the output error signal is used as input to the LMS algorithm. In the subband error based WTAF, the error signal in each subband is used as input to the LMS algorithm. The algorithms for the WTAF were also generalized in this work. In order to speed up the calculation, we developed the block LMS based WTAF, which modifies the weights of the adaptive filter block-by-block instead of sample-by-sample. Experimental studies were performed to study the performance of different implementation schemes for the WTAF. Simulations were performed on different WTAF algorithms with a sinusoidal input and with a pulse input. The speed and stability properties of each structure were studied experimentally and theoretically. It was found that different WTAF structures had different tradeoffs in terms of stability, performance, computational complexity, and convergence speed. The WTAF algorithms were applied to an online measurement system for fabric compressional behavior and they showed encouraging results. A 3-stage DWT based WTAF and a block WTAF based on a 3-stage DWT was employed to process the noisy force-displacement signal acquired from the online measurement system. The signal-to-noise ratio was greatly increased by applying these WTAFs, which makes a lower sampling rate a possibility. The reduction of the required time for data sampling and processing greatly improves the system speed to meet faster testing requirements. The WTAF algorithm could also be used in other applications requiring fast processing, such as in the real-time applications in communications, measurement, and control.

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Ghafoori, Elyar. "Wavelet transform and neural network." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527935.

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Automatic and accurate detection of Atrial Fibrillation (AF) from the noninvasive ECG signal is imperative in Electrocardiography. AF is mainly reflected in the ECG signal with the absence of P wave and/or irregular RR intervals. Signal processing tools can assess such detailed changes in the ECG, leading to an accurate diagnosis of AF. The proposed method relies on proper noise filtering, Stationary Wavelet Transform, and signal Power Spectrum Estimation. A feature extraction technique and a Neural Network classifier have been employed to determine the presence and absence of the AF episodes. Implementation of the proposed method with 5-fold cross validation on more than 230 hours of ECG data from MIT-BIH arterial fibrillation annotated database demonstrated an accuracy of 93% in classification of the AF and normal ECG signals.

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Xiao, Panrong. "Image compression by wavelet transform." [Johnson City, Tenn. : East Tennessee State University], 2001. http://etd-submit.etsu.edu/etd/theses/available/etd-0711101-121206/unrestricted/xiaop0720.pdf.

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Tieng, Quang Minh. "Wavelet transform based techniques for the recognition of objects in images." Thesis, Queensland University of Technology, 1996.

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Despite its short history, the wavelet transform has found application in a remarkable diversity of disciplines: Mathematics, Physics, Numerical Analysis,Signal Processing and others. In this thesis, we explore applications of this transform in image analysis and devise several algorithms for recognising objects in an image of a scene. Five different algorithms, consisting of representations and matching techniques, have been proposed for handling different kinds of objects in different situations.
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Grzeszczak, Aleksander. "VLSI architecture for Discrete Wavelet Transform." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9908.

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In this thesis, we present a new simple and efficient VLSI architecture (DWT-SA) for computing the Discrete Wavelet Transform. The proposed architecture is systolic in nature, modular and extendible to 1-D or 2-D DWT transform of any size. The DWT-SA has been designed, simulated and implemented in silicon. The following are the features of the DWT-SA architecture: (1) It has an efficient (close to 100%) hardware utilization. (2) It works with data streams of arbitrary size. (3) The design is cascadable, for computation of one, two or three dimensional DWT. (4) It requires a minimum interface circuitry on the chip for purposes of interconnecting to a standard communication bus. The DWT-SA design has been implemented using CMOS 1.2 um technology.
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Mudry, Andrew H. "Speaker identification using the wavelet transform." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22123.pdf.

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Watkins, Lanier A. "Modulation characterization using the wavelet transform." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1997. http://digitalcommons.auctr.edu/dissertations/640.

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The focus of this research is to establish an Automatic Modulation Identifier (AMI) using the Continuous Wavelet Transform (CWT) and several different classifiers. A Modulation Identifier is of particular interest to the military, because it has the potential to quickly discriminate between different communication waveforms. The CWT is used to extract characterizing information from the signal, and an artificial Neural Network is trained to identify the modulation type. Various analyzing wavelets and various classifiers were used to assess comparative performance. The analyzing wavelets used were the Mexican Hat Wavelet, the Morlet Wavelet, and the Haar Wavelet. The variety of classifiers used were the Multi-Layer Perceptron, the K-Nearest Neighbor and the Fuzzy Artmap. The CWT served as a preprocessor, and the classifiers served as an identifier for Binary Phase Shift Keying (BPSK), Binary Frequency Shift Keying (BFSK), Binary Amplitude Shift Keying (BASK), Quadature Phase Shift Keying (QPSK), Eight Phase Shift Keying (8PSK), and Quadature Amplitude Modulation (QAM) signals. Separation of BASK, BFSK and BPSK was performed in part one of the research project, and separation of BPSK, QPSK, 8PSK, BFSK, and QAM comprised the second part of the project. Each experiment was performed for waveforms corrupted with Additive White Gaussian Noise ranging from 20 dB - 0 dB carrier to noise ratio (CNR). To test the robustness of the technique, part one of the research project was tested upon several carrier frequencies w/2, and w/3 which was different from the carrier frequency w that the classifiers were trained upon. In the separation of BASK, BFSK and BPSK, the AMI worked extremely well (100% correct classification) down to 5 dB CNR tested at carrier frequency w, and it worked well (80% correct classification) down to 5 dB CNR tested at carrier frequencies w/2, and w/3. In the separation of BPSK, QPSK, 8PSK, BFSK, and QAM, the AMI performed very well at 10 dB CNR (98.8% correct classification). Also a hardware design in the Hewlet Packard Visual Engineering Environment (HP-VEE) for implementation of the AMI algorithm was constructed and is included for future expansion of the project.
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Mudry, Andrew H. (Andrew Henry) Carleton University Dissertation Engineering Electronics. "Speaker identification using the wavelet transform." Ottawa, 1997.

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Books on the topic "Wavelet transform"

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Pathak, Ram Shankar. The Wavelet Transform. Paris: Atlantis Press, 2009. http://dx.doi.org/10.2991/978-94-91216-24-4.

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The wavelet transform. Amsterdam: Atlantis Press/World Scientific, 2009.

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

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Addison, Paul S. The Illustrated Wavelet Transform Handbook. Second edition. | Boca Raton, FL : CRC Press, Taylor & Francis: CRC Press, 2017. http://dx.doi.org/10.1201/9781315372556.

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Shukla, K. K., and Arvind K. Tiwari. Efficient Algorithms for Discrete Wavelet Transform. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4941-5.

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Kaarna, Arto. Multispectral image compression using the wavelet transform. Lappeenranta, Finland: Lappeenranta University of Technology, 2000.

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1973-, La Cour-Harbo A., ed. Ripples in mathematics: The discrete wavelet transform. Berlin: Springer, 2001.

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Discrete wavelet transform: A signal processing approach. Chichester, UK: John Wiley & Sons, 2015.

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Mustapa, N. A demonstration for the wavelet transform of images. Manchester: UMIST, 1994.

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Rabinovitch, Ido. High quality image compression using the wavelet transform. Ottawa: National Library of Canada, 1996.

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Book chapters on the topic "Wavelet transform"

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Vyas, Aparna, Soohwan Yu, and Joonki Paik. "Wavelets and Wavelet Transform." In Signals and Communication Technology, 45–92. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7272-7_3.

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Zhang, Dengsheng. "Wavelet Transform." In Texts in Computer Science, 35–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_3.

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Mehra, Mani. "Wavelet Transform." In Forum for Interdisciplinary Mathematics, 95–105. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2595-3_5.

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Vistnes, Arnt Inge. "Wavelet Transform." In Physics of Oscillations and Waves, 475–510. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72314-3_14.

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Layer, Edward, and Krzysztof Tomczyk. "Wavelet Transform." In Signal Transforms in Dynamic Measurements, 97–105. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13209-9_5.

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Zhang, Dengsheng. "Wavelet Transform." In Texts in Computer Science, 45–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69251-3_3.

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Pachori, Ram Bilas. "Wavelet Transform." In Time-Frequency Analysis Techniques and their Applications, 89–118. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003367987-5.

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Gao, Robert X., and Ruqiang Yan. "Continuous Wavelet Transform." In Wavelets, 33–48. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1545-0_3.

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Gao, Robert X., and Ruqiang Yan. "Discrete Wavelet Transform." In Wavelets, 49–68. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1545-0_4.

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Gao, Robert X., and Ruqiang Yan. "Wavelet Packet Transform." In Wavelets, 69–81. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1545-0_5.

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Conference papers on the topic "Wavelet transform"

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Sheng, Yunglong, Danny Roberge, Taiwei Lu, and Harold Szu. "Optical wavelet matched filters." In 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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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. 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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Li, Kun, Qionghai Dai, and Wenli Xu. "Color transfer based on wavelet transform." In Electronic Imaging 2008, edited by William A. Pearlman, John W. Woods, and Ligang Lu. SPIE, 2008. http://dx.doi.org/10.1117/12.762238.

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Adel Gabry, Mohamed, Ibrahim Eltaleb, M. Y. Soliman, S. M. Farouq-Ali, Paul J. Cook, Florian A. Soom, and Yves Guglielmi. "Validation of Continuous Wavelet Transform Closure Detection Technique Using Strain Measurements." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212360-ms.

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Abstract Although closure detection has a crucial role in hydraulic fracturing operations, significant debate surrounds the various methodologies to determine its value. Several competing methodologies have been presented in the literature that sometimesyield significantly different estimates of closure pressure and time. The conventional techniques rely on assumptions that may be competing or even contradictory. The continuous wavelets transform technique is a data transform technique that convolves the pressure and/or temperature data using a short wavy signal called "wavelet". The wavelet transform provides a representation of the pressure signal by letting the translation and scale parameters of the wavelets vary continuously. That enables the analyst to find the details of the pressure data by observing the wavelet energy spectrum for the monitored signal (pressure and/or temperature) signal. In this case the event of contact between two fracture faces and complete fracture closure is clearly identified. As a part of The EGS Collab project, a series of fracture injection tests have been conducted to estimate the minimum principal stress with direct observation of well bore deformation using the SIMFIP tool (Step-Rate Injection Method for Fracture In-Situ Properties). The tool monitors the deformation using strain gauges as a fracture opens and closes during multiple tests. The publicly available data provide a great opportunity to experimentally calibrate the new technique for detecting the closure event using continuous wavelet transform. The effect of fracture closure events and fracture faces contact events detected using continuous wavelet transform were compared to the experimental measured deformation. The continuous wavelet transform technique for closure detection showed an agreement with the deformation measurement. The effect of the presence of natural fractures and complex fracture closure events were recognized using the continuous wavelet transform technique. The Contineous Wavelet Transform (CWT) is a global technique that can be applied to the pressure decline data without requiring further information about the reservoir geomechanical parameters or pumping data. The technique can be easily embedded in machine learning algorithms for hydraulic fracturing diagnostics.
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Kulkarni, Jyoti S. "Wavelet transform applications." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941550.

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Jun-Hai Zhai and Su-Fang Zhang. "Image denoising via wavelet threshold: single wavelet and multiple wavelets transform." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527500.

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Jauregui, Juan C., Eduardo Rubio, and Oscar Gonzalez. "Non-Linear Rotor-Rubbing Vibration Analysis Through the Wavelet Transform." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27417.

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Vibration analysis is the basis for an early detection of faults in rotating machinery. It is the main element for any maintenance program. Due to the basics of the FFT (Fast Fourier Transform), vibration prediction systems based on this analysis tool are unable to identify unsteady and non-linear vibrations. They provide only the spectrum content of a signal, but due to its nature, the time interval and the instant when the vibration occurs are eliminated. Therefore, new modern signal analysis tools have been developed. Among them, wavelets have been extensively applied to non-linear vibration and transient analysis. In the case of a rotor rubbing the housing or the bearing, wavelets allow the identification of the “wave shape” through a predetermined time-frequency function. The challenge of this method is the definition of the function that represents the actual phenomenon. Results show that rubbing is a non-linear phenomenon, this was verified through a ramp-up and ramp-down test procedure. It was found that the most appropriate procedure for its identification is through wavelet analysis. The key factor is the definition of the best-fit wavelet.
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Chou, Mo-Hong, and EnBing Lin. "Wavelet compression through wavelet Stieltjes transform." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Andrew F. Laine. SPIE, 1993. http://dx.doi.org/10.1117/12.162085.

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Aretakis, N., and K. Mathioudakis. "Wavelet Analysis for Gas Turbine Fault Diagnostics." In ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-gt-343.

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The application of wavelet analysis to diagnosing faults in Gas Turbines is examined in the present paper. Applying the Wavelet Transform to time signals obtained from sensors placed on an engine, gives information which is in correspondence to their Fourier Transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the Wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.
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Taheri, Shahyar, and Saied Taheri. "Rail Track Defect Detection Using Derivative Wavelet Transform." In ASME 2012 Rail Transportation Division Fall Technical Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/rtdf2012-9415.

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Railroad track monitoring systems are used for finding rail defects that may lead to a derailment of the train. The classical limit-value based defect detection systems are simple but are limited in their capability to detect small defects. As a cutting-edge supervision method, signal derivative filters can help to reveal information in the acceleration signal collected while the train is moving on the rail. The derivative filters are designed based on the required performance of the application. However, their design should be done with caution because they can greatly amplify the noise in the data, especially in high frequencies. Derivative filters can be implemented in the sample domain of space or time. The derivative filters in time domain are not always sufficient to study all the features of a signal. To explore the signal content, wavelet transformation was chosen, because it gives accurate description of the frequency contents according to their position in time. It should be noted that the wavelet transform that gives the derivative of a signal, has the properties of smoothing and differentiation. The proposed algorithm processes the data using continuous and discrete derivative wavelets filters, and is able to locate defects and provide information that may help to distinguish between various types of rail and wheel defects, including rail cracks, squats, corrugation, and wheel out-of-rounds. The wavelet-based algorithm developed was applied to a sample accelerometer signal and the results show the potential of this algorithm to locate and diagnose defects from limited bogie vertical acceleration data.
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Reports on the topic "Wavelet transform"

1

Kozaitis, Samuel P. Optical Wavelet Transform. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada339152.

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Shensa, M. J. The Discrete Wavelet Transform. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada239642.

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Subotic, N. S., L. Collins, M. Reiley, B. Thelen, and J. Gorman. Wavelet Transform Based Target Detection. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada303470.

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Yerdelen, Cehit, and Mohamed Abdelkader. Hydrological Data Trend Analysis with Wavelet Transform. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2021. http://dx.doi.org/10.7546/crabs.2021.08.11.

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Lin, Xueming. ERP Analysis Using Matched Filtering and Wavelet Transform. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6941.

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Qi, Hong. Pattern Recognition and ERP Waveform Analysis Using Wavelet Transform. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6507.

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

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Hewer, Gary A., and Wei Kuo. Wavelet Transform of Fixed Pattern Noise in Focal Plane Arrays. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada276963.

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Murenzi, Romain, Lance Kaplan, Jean-Pierre Antoine, and Fernando Mujica. Computational Complexity of the Continuous Wavelet Transform in Two Dimensions. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada358633.

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Drumheller, David M. Theory and Application of the Wavelet Transform to Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada239533.

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