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Relevant bibliographies by topics / Optimal wavelet basis (OWB)

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Academic literature on the topic 'Optimal wavelet basis (OWB)'

Author: Grafiati

Published: 2 June 2025

Last updated: 5 July 2025

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Journal articles on the topic "Optimal wavelet basis (OWB)"

1

Anakha, Satheesh P* Dr. D. Loganathan. "DE-SPECKLING OF SAR IMAGES BASED ON OPTIMAL BASIS WAVELET VIA PATCH ORDERING." Global Journal of Engineering Science and Research Management 3, no. 6 (2016): 49–55. https://doi.org/10.5281/zenodo.55960.

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Synthetic Aperture Radar (SAR) technology has mainly used for capturing high quality images from higher altitudes. SAR imagery has become an important application over optical satellite imagery because of its ability to operate in any whether condition. The SAR image acquired via coherent imaging are associated with a noise called speckle noise, which is multiplicative in nature. The presence of speckle noise degrades the quality of SAR image then leads to loss of crucial information. So it has become very important to remove the speckle noise from SAR images using suitable techniques. Many different SAR image-despeckling techniques proposed over past few years. In this paper, proposed a new idea for de-speckling the SAR image to the maximum and the proposed method achieves state-of-the-art de-speckling performance.

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Gu, Xiangping, Mingxue Zhu, and Liyun Zhuang. "Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT Networks." Sensors 21, no. 20 (2021): 6899. http://dx.doi.org/10.3390/s21206899.

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One of the major concerns in 5G IoT networks is that most of the sensor nodes are powered through limited lifetime, which seriously affects the performance of the networks. In this article, Compressive sensing (CS) technique is used to decrease transmission cost in 5G IoT networks. Sparse basis is one of the important steps in the CS. However, most of the existing sparse basis-based method such as DCT (Discrete cosine transform) and DFT (Discrete Fourier Transform) basis do not capture data structure characteristics in the networks. They also do not take into consideration multi-resolution representations. In addition, some of sparse basis-driven methods exploit either spatial or temporal features, resulting in performance degradation of CS-based strategies. To address these challenging problems, we propose a novel spatial–temporal correlation basis algorithm (SCBA). Subsequently, an optimal basis algorithm (OBA) is provided considering greedy scoring criteria. To evaluate the efficiency of OBA, orthogonal wavelet basis algorithm (OWBA) by employing NS (Numerical Sparsity) and GI (Gini Index) sparse metrics is also presented. In addition, we discuss the complexity of the above three algorithms, and prove that OBA has low numerical rank. After experimental evaluation, we found that OBA is capable of the sparsest representing original signal compared to spatial, DCT, haar-1, haar-2, and rbio5.5. Furthermore, OBA has the low recovery error and the highest efficiency.

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He, Can, Jian Chun Xing, and Qi Liang Yang. "Optimal Wavelet Basis Selection for Wavelet Denoising of Structural Vibration Signal." Applied Mechanics and Materials 578-579 (July 2014): 1059–63. http://dx.doi.org/10.4028/www.scientific.net/amm.578-579.1059.

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Wavelet basis selection is an important part in the wavelet denoising of structural vibration signal. However, some defects are present in the existing methods, such as large computation and a single optimal index. In order to solve these problems, a new selection method based on multiple index is proposed in this paper. Firstly, the wavelet basis category which suits for the vibration signal denoising is determined by analyzing the characteristics of wavelet basis and vibration signal. Then, a multiple index evaluation function is constructed by mean square error indicator (MSE), signal-to-noise ratio indicator (SNR) and correlation coefficient indicator (ρ), the weights of index are received by analytic hierarchy process (AHP), the wavelet basis with biggest evaluation function value is considered as optimal wavelet basis. At the end of the paper, a experiment is provided to verify the effectiveness of the new method, the results show that the new method is better than the other four methods in MSE, SNR and ρ index.

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Saini, Manish Kumar, Rajiv Kapoor, Ajai Kumar Singh, and Manisha. "Performance Comparison between Orthogonal, Bi-Orthogonal and Semi- Orthogonal Wavelets." Advanced Materials Research 433-440 (January 2012): 6521–26. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6521.

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The main work in the wavelet analysis is to find a good wavelet basis to perform an optimal decomposition. The goal of the proposed study is to obtain a basis function that can give optimal information from PQ signal. The study presents the wavelet basis to obtain the reconstruction and decomposition filter coefficients for orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. In this study, the task is to choose better wavelet basis which has been used for PQ signal compression or decomposition among orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. Certain criterion have been adopted to decide the best basis for the decomposition of the PQ signal which are as energy compaction ratio (ECR), absolute mean square error (AMSE), percent residual difference (PRD) and peak signal to noise ratio (PSNR). Numbers of experiments have been performed on real time PQ signal. The comparisons have been made in tabular form to choose the best wavelet basis.

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Wang, Yaming, Jianmin Cheng, Junbao Zheng, Yingli Xiong, and Huaxiong Zhang. "Analysis of wavelet basis selection in optimal trajectory space finding for 3D non-rigid structure from motion." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (2014): 1450023. http://dx.doi.org/10.1142/s0219691314500234.

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Trajectory representation model has been proposed to describe non-rigid deformation. An optimal trajectory space finding algorithm for 3D non-rigid structure from motion (OTSF-NRSFM) based on this model also has been proposed. However, the influence of the wavelet basis selection on the OTSF-NRSFM algorithm has still not been studied. To help OTSF-NRSFM researchers select wavelet basis properly, we investigated the influences of wavelet basis selection. Two typical wavelet bases, DCT basis and WHT basis, are discussed in this paper. The spectrum properties of wavelet basis and feature point trajectory, trajectory representation results on synthetic shark data, OTSF-NRSFM reconstruction results on synthetic data and real data are analyzed. The results show that the wavelet selection has much influence on OTSF-NRSFM reconstruction results of some non-rigid feature points, which have complicated trajectory. This paper gives researchers some inspiration about wavelet basis selection in OTSF-NRSFM algorithm.

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Mahdavi, Seyed Hossein, and Hashim Abdul Razak. "A Comparative Study on Optimal Structural Dynamics Using Wavelet Functions." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/956793.

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Wavelet solution techniques have become the focus of interest among researchers in different disciplines of science and technology. In this paper, implementation of two different wavelet basis functions has been comparatively considered for dynamic analysis of structures. For this aim, computational technique is developed by using free scale of simple Haar wavelet, initially. Later, complex and continuous Chebyshev wavelet basis functions are presented to improve the time history analysis of structures. Free-scaled Chebyshev coefficient matrix and operation of integration are derived to directly approximate displacements of the corresponding system. In addition, stability of responses has been investigated for the proposed algorithm of discrete Haar wavelet compared against continuous Chebyshev wavelet. To demonstrate the validity of the wavelet-based algorithms, aforesaid schemes have been extended to the linear and nonlinear structural dynamics. The effectiveness of free-scaled Chebyshev wavelet has been compared with simple Haar wavelet and two common integration methods. It is deduced that either indirect method proposed for discrete Haar wavelet or direct approach for continuous Chebyshev wavelet is unconditionally stable. Finally, it is concluded that numerical solution is highly benefited by the least computation time involved and high accuracy of response, particularly using low scale of complex Chebyshev wavelet.

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Kai Hu, Aiguo Song, Dan Mao, Ling Tan, and Le Yang. "An Optimal wavelet basis for ECG Compressed Sensing." International Journal of Digital Content Technology and its Applications 7, no. 7 (2013): 594–602. http://dx.doi.org/10.4156/jdcta.vol7.issue7.70.

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Lu, Binjie, and Xiaobing Zhang. "Improved wavelet threshold denoising method for magnetic field signals of magnetic targets." Measurement Science and Technology 36, no. 3 (2025): 036105. https://doi.org/10.1088/1361-6501/adafcc.

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Abstract The presence of complex electromagnetic noise significantly impacts the accuracy of magnetic targets signal detection, necessitating the development of an effective denoising method to enhance detection precision. Nevertheless, traditional denoising methods faces problems such as difficulty in selecting wavelet basis, difficulty in specifying the decomposition level, and unreasonable selection of thresholds. This study introduces improved wavelet threshold denoising based on peak-to-sum ratio and composite evaluation index T, named as (PSR-T-IWTD). PSR-T-IWTD integrates the improved wavelet basis selection method, improved wavelet decomposition level selection method, improved threshold selection method, and improved threshold function design method. Calculate the composite evaluation index T and select the wavelet basis with the smallest T as the optimal wavelet basis. The optimal number of decomposition level is determined by the PSR of the wavelet detail coefficients. An improved threshold selection method and threshold function are introduced to further enhance the performance of wavelet threshold denoising (WTD). Finally, the magnetic field denoising test of the ship model was designed and compared with Butterworth low-pass filter (BLPF), optimal wavelet selection wavelet adaptive threshold denoising (OWSWATD) and improved WTD based on T (T-IWTD) to verify the effectiveness of PSR-T-IWTD. The test results show that PSR-T-IWTD has lower computational complexity. Meanwhile, PSR-T-IWTD improves the signal-to-noise ratio by 10.2%, 6.8% and 8.3% compared to BLPF, OWSWATD and T-IWTD, respectively.

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Zemtsov, A. N. "Multiscale Analysis of High Resolution Digital Elevation Models Using the Wavelet Transform." Scientific Visualization 16, no. 2 (2024): 1–10. http://dx.doi.org/10.26583/sv.16.2.01.

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A technique is proposed for choosing the optimal wavelet basis in terms of decorrelation of the spectral coefficients of the wavelet basis when solving the problem of representation of digital elevation models. In the course of the work, it was revealed that the selection of the spectral transform basis significantly affects the accuracy of the representation of the original model. The proposed method to the decomposition of digital elevation models based on the discrete wavelet transform does not require large computational costs. A technique is proposed for selection the optimal wavelet basis from the position of the minimum mean square error of the reconstructed signal, when quantizing the high-frequency expansion coefficients. Expressions are obtained for generating scaling and wavelet functions in space. The method developed to represent digital elevation models has good properties, which allows to significantly increase the resolution of digital elevation models in the implemented regional geoinformation system.

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Arkhipova, O. V., N. N. Dolgikh, S. Yu Dolinger, V. Z. Kovalev, and D. S. Osipov. "Wavelet transform algorithm of daily load graphs for choosing parameters of hybrid energy storage." Omsk Scientific Bulletin, no. 174 (2020): 57–62. http://dx.doi.org/10.25206/1813-8225-2020-174-57-62.

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The paper presents an algorithm for frequency decomposition of daily load graphs based on a discrete wavelet transform. This algorithm makes it possible to choose the optimal type of wavelet function, optimal level and wavelet decomposition tree. The inverse wavelet transform (recovery) along a single branch of the approximating coefficient allows obtaining the lowfrequency component of the power graph for selecting the optimal mode of the hybrid energy storage battery. The detailing branch of the wavelet coefficients determines the operating mode of the supercapacitor. A numerical experiment is built on the basis of data obtained using certified equipment

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Book chapters on the topic "Optimal wavelet basis (OWB)"

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Saxena, Shivani, and Ritu Vijay. "Optimal Selection of Wavelet Transform for De-noising of ECG Signal on the Basis of Statistical Parameters." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_67.

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Balakin, Dmitry, Vitaly Shtykov, Alexey Zubko, Shalimova Elena Vladimirovna, and Zayed Saleh Salem Ali. "Principles of Diagnosing the Technical Condition of the Bearings of the Gas Turbine Engine Supports Using Rhythmogram and Scatterogram." In Advances in Turbomachinery [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108400.

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The possibility of using a rhythmogram and a scatterogram for bearings of diagnosing a gas turbine engine and its components is discussed. Rhythmogram and scatterogram evaluate the quasi-periodicity of the technical system of a gas turbine engine. Rhythmogram and scatterogram were obtained using the method developed by us for processing quasi-periodic pulse signals. The method is based on the principles of the theory of optimal filtering, the theory of wavelet transform, and the Hermite transform. The wavelet transform is considered as a cross-correlation function. The Gauss-Hermite functions are used as the basis for wavelet analysis. The effectiveness of the diagnostic method is demonstrated by the example of the operation of the bearing supports of a gas turbine engine and the engine as a whole.

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Panda, Mrutyunjaya, Aboul Ella Hassanien, and Ajith Abraham. "Hybrid Data Mining Approach for Image Segmentation Based Classification." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch064.

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Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

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Conference papers on the topic "Optimal wavelet basis (OWB)"

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Chao, Tien-Hsin, Araz Yacoubian, Brian Lau, and William J. Miceli. "Optical Wavelet Processor for Target Detection." In Optical Computing. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.owb5.

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Wavelet transform has been widely applied to time-frequency signal analysis, image processing (enhancement, feature extraction, etc.), and target detection. Since wavelet transform is a convolution process between an input and a large number of wavelet bases, the computation load increases nonlinearly with the sizes of the input and the wavelet. Near real-time optical wavelet transform [1-3] could be accomplished by using an optical correlator architecture. The processing speed of optical wavelet transform is independent of the size of the wavelet filter and is only limited to the updating speed of the spatial light modulator.

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2

Zhuang, Yan, and John S. Baras. "Image compression using optimal wavelet basis." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Harold H. Szu. SPIE, 1995. http://dx.doi.org/10.1117/12.205380.

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Zhuang, Yan, and John S. Baras. "Optimal wavelet basis selection for signal representation." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Harold H. Szu. SPIE, 1994. http://dx.doi.org/10.1117/12.170025.

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Cheng, Liwei, Duanling Li, Gongjing Yu, Zhonghai Zhang, and Shuyue Yu. "The Optimal Wavelet Basis for Electroencephalogram Denoising." In ISAIMS 2020: 2020 International Symposium on Artificial Intelligence in Medical Sciences. ACM, 2020. http://dx.doi.org/10.1145/3429889.3429906.

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Li, Suyi, Yanju Ji, and Guangda Liu. "Optimal Wavelet Basis Selection of Wavelet Shrinkage for ECG De-Noising." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5303109.

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Tashakkori, Rahman, John M. Tyler, and Oleg S. Pianykh. "Construction of optimal wavelet basis for medical images." In AeroSense '99, edited by Harold H. Szu. SPIE, 1999. http://dx.doi.org/10.1117/12.342925.

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Deng, Na, and Chang-sen Jiang. "Selection of optimal wavelet basis for signal denoising." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234211.

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Murphy, D. D. "BER estimation for wavelet packet modulation schemes and optimal basis selection." In IEE Irish Signals and Systems Conference 2005. IEE, 2005. http://dx.doi.org/10.1049/cp:20050306.

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Wei, Dong, and Alan C. Bovik. "Enhancement of decompressed images by optimal shift-invariant wavelet packet basis." In Electronic Imaging: Science & Technology, edited by Robert L. Stevenson, Alexander I. Drukarev, and Thomas R. Gardos. SPIE, 1996. http://dx.doi.org/10.1117/12.234761.

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Xu, Licheng, and Misheng Xue. "Selection of optimal wavelet basis for singularity detection of non-stationary signal." In 2011 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2011. http://dx.doi.org/10.1109/iceceng.2011.6057359.

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