The Approximation of Nonlinear Function using Daubechies and Symlets Wavelets

Bahri, Syamsul; Awalushaumi, Lailia; Susanto, Marliadi

doi:10.5220/0008521103000306

Cited by 4 publications

(1 citation statement)

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“…Where  represents a mother wavelet, a represents the scaling (dilation) parameter that determines the degree of compression or scale, and b represents the translation parameter that specifies the time location of the wavelet [20]. Similarly, the wavelet also provides multi-resolution signal analysis, enabling the extraction of features that are sensitive to fine and coarse details in the signal.…”

Section: -1-1) Image Preprocessingmentioning

confidence: 99%

Wavelet Transform-based Methods for Forensic Analysis of Digital Images

Muhammad S. Mandisha,

Mohamed A. Hussien,

Amr K. Shalaby

et al. 2024

ARASET

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In recent years, Generative Adversarial Networks (GANs) have been utilized in many applications of our daily lives to create digital media that was previously impossible. This paper utilizes GANs in multimedia forensics, where the precision and accuracy of image classification are crucial. The objective of the study is to detect sophisticated manipulated images generated by advanced deep learning methods using classical transformation methods. The method combines the classical wavelet transform for feature extraction with a classifier model to distinguish between real and fake images. The proposed method extracts unique features that differentiate between fake and real images, which are then fed to several gradient-boosting-based classifiers. The proposed method was tested using the FaceForensics++ dataset, which contains low-resolution video sequences of faces. The proposed model achieves an accuracy of 95% in detecting manipulated videos compared with 87% accuracy of other classical techniques.

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Section: -1-1) Image Preprocessingmentioning

confidence: 99%

Wavelet Transform-based Methods for Forensic Analysis of Digital Images

Muhammad S. Mandisha,

Mohamed A. Hussien,

Amr K. Shalaby

et al. 2024

ARASET

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Multipath Mitigation for Single Frequency Stand-Alone Receivers Using Wavelet Denoising

Zulfiqar

Farooq

2023

2023 4th International Conference on Advancements in Computational Sciences (ICACS)

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Application of Compressed Sensing Based on Adaptive Dynamic Mode Decomposition in Signal Transmission and Fault Extraction of Bearing Signal

Dang

2022

Machines

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Bearings are widely used in mechanical equipment; nevertheless, potential dangers are also widespread, making bearing fault detection very important. For large equipment, the amount of collected signals tends to be huge, which challenges both signal transmission and storage. To solve this problem, compressed sensing (CS), based on specific dynamic modes of adaptive truncated rank dynamic mode decomposition (ADMD), is adopted to achieve the purpose of compressing and transmitting the signal, as well as extracting fault features. Firstly, this paper has proposed a new fitness function, which is called the synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the best truncated rank adaptively. Then, the historical signal attempts to be decomposed into a series of temporal and spatial coherent modes, through ADMD, and those modes are filtered and cascaded into a highly self-adaptive dictionary, the DMD dictionary, which approximates the original signal with some error. Next, CS is employed to compress and reconstruct the signal, in order to reduce storage space and improve transmission efficiency. Finally, signals of high quality can be reconstructed through orthogonal matching pursuit (OMP) algorithm. Compared with traditional dictionaries, the DMD dictionary, based on the mode structure generated by ADMD decomposition, ass proposed in this paper, can better represent the original signal in the simulation signal and have good noise reduction performance. The correlation coefficient (CORR) between the reconstructed signal and noise signal is 0.8109, between the reconstructed signal and non-noise signal is 0.9278, and the root mean square error (RMSE) is 0.0659 and 0.0351, respectively. Compared with the traditional SVD and EMD denoising methods, ADMD-CS has better noise reduction performance. In this paper, the signal-to-noise ratio (SNR) is taken as the quantitative indicator of denoising performance. It is found that the SNR of simulation signal and experimental signal processed by ADMD-CS is higher than that of the traditional denoising methods, which is 0.3017 and 0.8407, respectively. The storage space of the signal is quite smaller than traditional methods, and the compression ratios (CR) of the simulation and experimental signals are 66.16% and 59.08%, respectively. In conclusion, ADMD-CS has a good application prospect in signal transmission, storage, and feature extraction.

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The Approximation of Nonlinear Function using Daubechies and Symlets Wavelets

Cited by 4 publications

References 0 publications

Wavelet Transform-based Methods for Forensic Analysis of Digital Images

Wavelet Transform-based Methods for Forensic Analysis of Digital Images

Multipath Mitigation for Single Frequency Stand-Alone Receivers Using Wavelet Denoising

Application of Compressed Sensing Based on Adaptive Dynamic Mode Decomposition in Signal Transmission and Fault Extraction of Bearing Signal

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