Results 31 to 40 of about 36,871 (305)
The Journal of Engineering, 2019 In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction
Jun Wang +3 more
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Hybrid IIR/FIR Wavelet Filter Banks for ECG Signal Denoising [PDF]
, 2018 ElectroCardioGram (ECG) signals are usually corrupted with various types of artifacts which degrade the signal quality and might lead to misdiagnosis.
Eminaga, Y., Kale, I., Coskun, A.
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Rehaussement du signal de parole voisé par filtrage adaptatif des modes intrinsèques empiriques [PDF]
, 2010 In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced.
TURKI, Monia +2 more
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A time-frequency denoising method for single-channel event-related EEG
Frontiers in Neuroscience, 2022 IntroductionElectroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG).
Wenqiang Yan, Yongcheng Wu
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A multiresolution framework for local similarity based image denoising [PDF]
, 2012 In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity.
Rajpoot, Nasir M. (Nasir Mahmood) +1 more
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Wavelet Threshold Ultrasound Echo Signal Denoising Algorithm Based on CEEMDAN
, 2023 In this study, an algorithm for denoising ultrasound echo signals in industrial settings is proposed to address the problem of high noise and low signal-to-noise ratio.
Huyue Xu +3 more
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A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
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Wavelet shrinkage using adaptive structured sparsity constraints [PDF]
, 2015 Structured sparsity approaches have recently received much attention in the statistics, machine learning, and signal processing communities. A common strategy is to exploit or assume prior information about structural dependencies inherent in the data ...
Tomassi, Diego Rodolfo +7 more
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Sensors, 2017 As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of
Yuxing Li, Yaan Li, Xiao Chen, Jing Yu
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A novel denoising method for non‐linear and non‐stationary signals
IET Signal Processing, 2023 Signal denoising is a crucial step in signal analysis. Various procedures have been attempted by researchers to remove the noise while preserving the effective components of the signal. One of the most successful denoising methods currently in use is the
Honglin Wu +6 more
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