Improved Wavelet Denoising by Non-Convex Sparse Regularization Under Double Wavelet Domains

Wu, Yongjun; Gao, Guangjun; Cui, Can

doi:10.1109/access.2019.2903125

Cited by 30 publications

(17 citation statements)

References 49 publications

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“…Both the time and frequency window can be adjusted. Hence, it has a strong ability to extract local features of signals and detect singularity in signals [39]. The denoised signals can be obtained by employing the threshold function to filter the wavelet coefficients and conduct the wavelet reconstruction [40].…”

Section: Wavelet Packet Denoising Principlementioning

confidence: 99%

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Niu

Zang

et al. 2021

Atmosphere

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Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

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Section: Wavelet Packet Denoising Principlementioning

confidence: 99%

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Niu

Zang

et al. 2021

Atmosphere

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show abstract

“…In the process of wavelet denoising, there are two parameters that hinder its performance: (1) the choice of wavelet basis function and (2) setting of threshold function [20]. In the selection of wavelet basis function, Haar wavelet which is suitable for signal continuity is chosen in this study.…”

Section: Wavelet Denoising (Wd)mentioning

confidence: 99%

An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System

Wang

Mei

Liang

et al. 2021

Mobile Information Systems

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Channel estimation is the key technology to ensure reliable transmission in orthogonal frequency division multiplexing (OFDM) system. In order to improve the accuracy of the channel estimation algorithm in a low signal-to-noise ratio (SNR) channel environment, in this paper, we proposed an improved channel estimation algorithm based on the transform domain. The improved algorithm with wavelet denoising (WD) and distance decision analysis (DDA) to perform secondary denoising on the channel estimation algorithm based on the transform domain is proposed. First, after the least-squares (LS) algorithm, WD is used to denoise for the first time, then the DDA is used to further suppress the residual noise in the transform domain, and the important channel taps are screened out. Simulation results show that the proposed algorithm can improve the detection performance of existing channel estimation algorithms based on transform domain in low SNR.

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“…The wavelet transform technique (WT) is perfect in describing features of non-stationary signals. The technique is used in many applications such as image processing, signal processing, communication systems, time-frequency analysis and pattern recognition [61]- [65]. The WT decomposes signal into wavelets of several scales in the time-domain with changing window sizes, with each scale representing a particular feature of the signal under review.…”

Section: B Wavelet Transform (Wt)mentioning

confidence: 99%

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

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Improved Wavelet Denoising by Non-Convex Sparse Regularization Under Double Wavelet Domains

Cited by 30 publications

References 49 publications

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

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