Gotowe bibliografie tematyczne / Wavelet Transform and Wavelet Thresholding

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Gotowa bibliografia na temat „Wavelet Transform and Wavelet Thresholding”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Wavelet Transform and Wavelet Thresholding"

Umam, Ahmad Khairul, Pukky Tetralian Bantining Ngastiti, Aris Alfan, Zaqiyatus Shahadah, and Amanda Fatma Muamalah. "TRANSFORMASI WAVELET DISKRIT UNTUK DENOISING CITRA." MATHunesa: Jurnal Ilmiah Matematika 12, no. 2 (2024): 374–80. https://doi.org/10.26740/mathunesa.v12n2.p374-380.

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Nowadays, topic of wavelet has many applications including image denoising. Wavelet Transform is divided into discrete wavelet transform and continuous wavelet transform. Besides for image denoising, it can also useful for image compression and others. In this research is discussed about steps image denoising using wavelet. Wavelets that used are Haar, Daubechies, biorthogonal, symlets and coiflets wavelets for hard thresholding and soft thresholding. Program is made according to steps/algorithm that were created. Then, we compare original image and result of image denoising. In this research,

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Raath, Kim C., Katherine B. Ensor, Alena Crivello, and David W. Scott. "Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding." Entropy 25, no. 11 (2023): 1546. http://dx.doi.org/10.3390/e25111546.

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Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method

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ČASTOVÁ, NINA, DAVID HORÁK, and ZDENĚK KALÁB. "DESCRIPTION OF SEISMIC EVENTS USING WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 03 (2006): 405–14. http://dx.doi.org/10.1142/s0219691306001336.

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This paper deals with engineering application of wavelet transform for processing of real seismological signals. Methodology for processing of these slight signals using wavelet transform is presented in this paper. Briefly, three basic aims are connected with this procedure:. 1. Selection of optimal wavelet and optimal wavelet basis B opt for selected data set based on minimal entropy: B opt = arg min B E(X,B). The best results were reached by symmetric complex wavelets with scaling coefficients SCD-6. 2. Wavelet packet decomposition and filtration of data using universal criterion of thresho

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Dahraoui, Nadia, M'hamed Boulakroune, S. Khelfaoui, S. Kherroubi, and Yamina Benkrima. "Effectiveness of Wavelet Denoising on Secondary Ion Mass Spectrometry Signals." East European Journal of Physics, no. 3 (September 4, 2023): 495–500. http://dx.doi.org/10.26565/2312-4334-2023-3-56.

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Wavelet theory has already achieved huge success. For Secondary Ions Mass Spectrometry (SIMS) signals, denoising the secondary signal, which is altered by the measurement, is considered that an essential step prior to applying such a signal processing technique that aims enhance the SIMS signals.The most efficient and widely used wavelet denoising method is based on wavelet coefficient thresholding. This process involves three important steps; wavelet decomposition: the input signals are decomposed into wavelet coefficients, thresholding: the wavelet coefficients are modified according to a th

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Saidov, B. B., and V. F. Telezhkin. "Optimum ECG Signal Filtering Based on Wavelet Transformation." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 4 (2021): 167–72. http://dx.doi.org/10.14529/ctcr210415.

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The development of digital signal processing and microprocessor technology creates conditions for improving methods for diagnosing the functional state of organs. Wavelet analysis is a modern and promising method of information processing. In order to determine the effective optimal filtering of the electrocardiography signal based on the wavelet transform, wavelet filtering was performed using wavelets of different families, the efficiency of using different levels of decomposition, me¬thods for calculating the threshold and types of the threshold function was investigated. Aim. Determination

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Saood, Wesam Hujab, and Khamees Khalaf Hasan. "The Effectiveness of two Dimensional Haar Wavelet Image De-noising performance using Soft or Hard Thresholding Approach." Tikrit Journal of Pure Science 29, no. 1 (2024): 196–205. http://dx.doi.org/10.25130/tjps.v29i1.1507.

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Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement ..etc. this work is aimed at developing effective algorithm for denoising image using new strategy algorithm of wavelet techniques ,by applying two dimensions wavelet transform using Haar wavelet. Wavelets are hierarchically decomposing mathematical tools. A noisy picture is sent to the Haar wavelet transform to create four decomposed bands(for each level), and the noise is then removed by

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Ramadhan, Rahmat, and Agfianto Eko Putra. "Perbandingan Mother Wavelet dalam Proses Denoising pada Suara." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 69. http://dx.doi.org/10.22146/ijccs.3497.

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AbstrakTransformasi Wavelet telah digunakan dalam proses denoising pada suara dengan tujuan untuk meningkatkan kualitas dari rekaman suara yang tercampur dengan derau. Jenis-jenis derau yang terlibat antara lain White Gaussian Noise (WGN), White Uniform Noise (WUN) dan Colored Noise. Dalam penelitian ini dilakukan perbandingan terhadap beberapa mother wavelet, diantaranya Daubechies, Coiflet dan Symlet, dalam proses denoising pada sinyal suara yang diberikan WGN, WUN dan Colored Noise. Metode thresholding yang digunakan dalam proses denoising adalah Soft Thresholding dan nilai threshold berupa

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Shruti, Badgainya, Pankaj Sahu Prof., and Vipul Awasthi Prof. "Image Denoising for AWGN Corrupted Image Using OWT and Thresholding." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 220–26. https://doi.org/10.31142/ijtsrd18338.

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In this work, review of various well known algorithms for image denoising is carried out and their performances with their methodologies are comparatively assessed. A new algorithm based on the orthonormal wavelet transform OWT is developed. In this work images corrupted by AWGN are denoised. Simulation results shows that proposed method using Orthonormal wavelets for different values of noise Standard Deviation s in dB outperforms other available methods. Also Coiflet Wavelet performs better than Symlet, Haar and Daubechies wavelets. The proposed Orthonormal wavelet transform OWT method has m

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Nigam, Vaibhav, Smriti Bhatnagar, and Sajal Luthra. "Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity." Advanced Materials Research 403-408 (November 2011): 866–70. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.866.

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This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by compu

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Geetika Agotra and Prof. Manish Kumar Singhal. "A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 143–49. http://dx.doi.org/10.32628/ijsrst2411430.

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This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular

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Rozprawy doktorskie na temat "Wavelet Transform and Wavelet Thresholding"

Soares, Wendel Cleber [UNESP]. "Um método não-limiar para redução de ruído em sinais de voz no domínio wavelet." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/100309.

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Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-05-29Bitstream added on 2014-06-13T20:21:16Z : No. of bitstreams: 1 soares_wc_dr_ilha.pdf: 2948445 bytes, checksum: cf47c579c7e9a4f2d231373d9ed5f704 (MD5)<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>Neste trabalho é feito um estudo dos métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método não-limiar para redução de ruído em sinais de voz no domínio wavelet. Em geral os sinais de voz podem es

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Soares, Wendel Cleber. "Um método não-limiar para redução de ruído em sinais de voz no domínio wavelet /." Ilha Solteira : [s.n.], 2009. http://hdl.handle.net/11449/100309.

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Resumo: Neste trabalho é feito um estudo dos métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método não-limiar para redução de ruído em sinais de voz no domínio wavelet. Em geral os sinais de voz podem estar contaminados com ruídos artificiais ou reais. O problema consiste que dado um sinal limpo adiciona-se o ruído branco ou colorido, obtendo assim o sinal ruidoso, ambos no domínio do tempo. O que se propõe neste trabalho, é aplicar a transformada wavelet, obtendo assim o sinal transformado no domínio wavelet, reduzindo ou a

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Behjat, Hamid. "Statistical Parametric Mapping of fMRI data using Spectral Graph Wavelets." Thesis, Linköpings universitet, Medicinsk informatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81143.

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In typical statistical parametric mapping (SPM) of fMRI data, the functional data are pre-smoothed using a Gaussian kernel to reduce noise at the cost of losing spatial specificity. Wavelet approaches have been incorporated in such analysis by enabling an efficient representation of the underlying brain activity through spatial transformation of the original, un-smoothed data; a successful framework is the wavelet-based statistical parametric mapping (WSPM) which enables integrated wavelet processing and spatial statistical testing. However, in using the conventional wavelets, the functional d

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Herrmann, Felix J., and Gilles Hennenfent. "Non-linear data continuation with redundant frames." Canadian Society of Exploration Geophysicists, 2005. http://hdl.handle.net/2429/518.

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We propose an efficient iterative data interpolation method using continuity along reflectors in seismic images via curvelet and discrete cosine transforms. The curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). The advantage of using curvelets is that these frames are sparse for high-frequency caustic-free solutions of the wave-equation. Since we are dealing with less than ideal data (e.g. bandwidth-limited), we compliment the curvelet frames with th

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Čišecký, Roman. "Metody pro odstranění šumu z digitálních obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219769.

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The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application RapidMiner. In conclusion, the results obtained by different methods are compared.

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Amghar, Mohamed. "Multiscale local polynomial transforms in smoothing and density estimation." Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/262040.

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Un défi majeur dans les méthodes d'estimation non linéaire multi-échelle, comme le seuillage des ondelettes, c'est l'extension de ces méthodes vers une disposition où les observations sont irrégulières et non équidistantes. L'application de ces techniques dans le lissage de données ou l'estimation des fonctions de densité, il est crucial de travailler dans un espace des fonctions qui impose un certain degré de régularité. Nous suivons donc une approche différente, en utilisant le soi-disant système de levage. Afin de combiner la régularité et le bon conditionnement numérique, nous adoptons un

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Čáp, Martin. "Sledování trendů elektrické aktivity srdce časově-frekvenčním rozkladem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218005.

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Work is aimed at the time-frequency decomposition of a signal application for monitoring the EKG trend progression. Goal is to create algorithm which would watch changes in the ST segment in EKG recording and its realization in the Matlab program. Analyzed is substance of the origin of EKG and its measuring. For trend calculations after reading the signal is necessary to preprocess the signal, it consists of filtration and detection of necessary points of EKG signal. For taking apart, also filtration and measuring the signal is used wavelet transformation. Source of the data is biomedicine dat

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Mrázek, Jiří. "Filtrace signálů EKG pomocí vlnkové transformace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219528.

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This thesis deals myopotential denoising of ECG signals with using wavelet transform. There was used wavelet denoising subsequently wiener wavelet filtering. In both cases were found the most suitable coeficients for the best denoising. It is meant mainly settings suitable parameters for ideal filtration setting value of threshold, number of decomposition level, selection of thresholding and type of filter. These parameters are tested on real signals. Denoising is realized in Matlab version R2009b.

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Oliveira, Helder Cesar Rodrigues de. "Proposta de redução da dose de radiação na mamografia digital utilizando novos algoritmos de filtragem de ruído Poisson." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-29032016-160603/.

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O objetivo deste trabalho é apresentar um novo método para a remoção do ruído Poisson em imagens de mamografia digital adquiridas com baixa dosagem de radiação. Sabe-se que a mamografia por raios X é o exame mais eficiente para a detecção precoce do câncer de mama, aumentando consideravelmente as chances de cura da doença. No entanto, a radiação absorvida pela paciente durante o exame ainda é um problema a ser tratado. Estudos indicam que a exposição à radiação pode induzir a formação do câncer em algumas mulheres radiografadas. Apesar desse número ser significativamente baixo em relação ao nú

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T, Kovács Matúš. "Segmentace v mikroskopických obrazech z rostlinných preparátů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218723.

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This thesis deals with the segmentation of microscopic images from plant sections. It describes the importance of the histogram for obtaining information from the image, and the utilization of the wavelet transformation for the preprocessing of the images. The thesis describes and categorizes different segmentation methods. In the thesis we use MATLAB for the validation of the presented theories and as the interface for creating a software model. The created software application automatically analyzes and evaluates microscopic images.

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Książki na temat "Wavelet Transform and Wavelet Thresholding"

Jansen, Maarten. Noise Reduction by Wavelet Thresholding. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0145-5.

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

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Addison, Paul S. The Illustrated Wavelet Transform Handbook. 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. 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 University of Technology, 2000.

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

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

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

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

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Pham, Tuan Van. Wavelet analysis for robust speech processing and applications: Applications of discrete wavelet transform and wavelet denoising to speech enhancement and robust speech recognition. VDM, Verlag Dr. Müller, 2008.

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Części książek na temat "Wavelet Transform and Wavelet Thresholding"

Rajeswari, P., K. K. Thyagharajan, V. S. Prabhu, and G. Shree Devi. "Denoising X-Ray Image Using Discrete Wavelet Transform and Thresholding." In Futuristic Communication and Network Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4625-6_19.

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Pandit, Diptangshu, Li Zhang, Chengyu Liu, Nauman Aslam, Samiran Chattopadhyay, and Chee Peng Lim. "Noise Reduction in ECG Signals Using Wavelet Transform and Dynamic Thresholding." In Series in BioEngineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3957-7_10.

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Singh, Kumari Suniti, Yogesh Kumar Mishra, and Harsh Vikram Singh. "Secure Image Restoration and Contrast Enhancement Using Wavelet Transform and Thresholding Technique." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9775-3_75.

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Thi Hoang Hua, Yen, Giang Hong Nguyen, and Liet Van Dang. "Detection of Abnormalities in Mammograms by Thresholding Based on Wavelet Transform and Morphological Operation." In Intelligent Systems and Networks. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4725-6_60.

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Zhang, Dengsheng. "Wavelet Transform." In Texts in Computer Science. 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. 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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72314-3_14.

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

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Layer, Edward, and Krzysztof Tomczyk. "Wavelet Transform." In Signal Transforms in Dynamic Measurements. 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69251-3_3.

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Streszczenia konferencji na temat "Wavelet Transform and Wavelet Thresholding"

Wang, Lei, Wei Sun, Yibo Chen, Peng Li, and Lingxiao Zhao. "Wavelet Transform Based ECG Denoising Using Adaptive Thresholding." In the 2018 7th International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3239264.3239272.

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Huang, Zhi-Kai, De-Hui Liu, Xing-Wang Zhang, and Ling-Ying Hou. "Fusion Color Information for Image Thresholding Based Wavelet Transform." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.169.

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Fu, Xin, and Zhikai Huang. "Image segmentation histogram thresholding based on wavelet transform domain." In 2013 International Conference on Services Science and Services Information Technology. WIT Press, 2014. http://dx.doi.org/10.2495/sssit132132.

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Berri, Sidi M., and J. M. Klosner. "A New Strategy for Detecting Gear Faults Using Denoising With the Orthogonal Discrete Wavelet Transform (ODWT)." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8326.

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Abstract This paper investigates a new strategy for early detection of defects in a power transmission pair of spur gears. Sensitivity to local defects is enhanced by processing the signal as follows. The orthogonal discrete wavelet transform (ODWT) of the band-pass filtered averaged signal is first obtained. This is followed by thresholding in the wavelet domain, thereby removing the low amplitude noise contribution. The inverse wavelet transform then essentially reconstructs the component of the signal that is due to the defect. Experimental results demonstrate the efficiency of this procedu

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BEKHTIN, YU S., and V. T. CHIN. "WAVELET-BASED FILTERING OF NOISY IMAGES USING THEIR APPROXIMATIONS IN SUBBANDS OF DETAILS." In GRAPHICON 2024. Omsk State Technicl University, 2024. http://dx.doi.org/10.25206/978-5-8149-3873-2-2024-525-531.

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Image distortions can occur both under the influence of external factors and in the process of image formation due to the physical nature of various optoelectronic systems. To suppress broadband noise, many methods have been developed, among which filtering based on the wavelet transform occupies a special place due to the ability to effectively suppress both additive noise and also multiplicative noise. The methods of wavelet filtering widely used in practice are based on thresholding of wavelet coefficients which are belong in high frequency subbands named details. At the same time, the low

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Hong-Qiao Wang, Fu-Chun Sun, Yan-Ning Cai, and Zong-Tao Zhao. "Edge-enhanced speckle suppression using curvelet transform with an optimal soft thresholding." In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420664.

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Reddy, C. Somasundar, and C. Ravindra Murthy. "Image compression using complex wavelet transform (CWT) with custom thresholding." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8203693.

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Mitiche, Lahcene, Amel Baha Houda Adamou-Mitiche, and Hilal Naimi. "Medical image denoising using dual tree complex thresholding wavelet transform." In 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, 2013. http://dx.doi.org/10.1109/aeect.2013.6716477.

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Sangeetha, M., S. Nirmala Devi, and N. Kumaravel. "Wavelet transform based coronary blood vessel segmentation using entropy thresholding." In IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing. IEE, 2006. http://dx.doi.org/10.1049/cp:20060371.

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Chang, Charles C., Jyh-Charn S. Liu, and Andrew K. Chan. "Thresholding in a calculation algorithm for fast approximation of wavelet transform." In Optical Science, Engineering and Instrumentation '97, edited by Akram Aldroubi, Andrew F. Laine, and Michael A. Unser. SPIE, 1997. http://dx.doi.org/10.1117/12.292807.

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Raporty organizacyjne na temat "Wavelet Transform and Wavelet Thresholding"

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

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Schick, I. C., and H. Krim. Robust Wavelet Thresholding for Noise Suppression. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada458897.

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Shensa, M. J. The Discrete Wavelet Transform. Defense Technical Information Center, 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. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada303470.

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Abdallah, Mahmoud A., and Ram-Nandan P. Singh. Image Data Compression by Adaptive Thresholding of Wavelet Coefficients. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada375823.

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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, 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, 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, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada276963.

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