Results 1 to 10 of about 136,209 (163)
IEEE Access, 2017 Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information.
Zhipeng Feng, Dong Zhang, Ming J. Zuo
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EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis [PDF]
DiagnosticsBackground and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis.
Zhi-Yang Zhao +6 more
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The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal
Sensors, 2021 The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system.
Young In Jang +3 more
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Signal Separation Operator Based on Wavelet Transform for Non-Stationary Signal Decomposition [PDF]
SensorsThis paper develops a new frame for non-stationary signal separation, which is a combination of wavelet transform, clustering strategy and local maximum approximation.
Ningning Han, Yongzhen Pei, Zhanjie Song
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Error reduction in EMG signal decomposition.
J Neurophysiol, 2014 Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors.
Kline JC, De Luca CJ.
europepmc +4 more sources
A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark [PDF]
Sensors, 2021 Cecilia Surace +2 more
exaly +2 more sources
Temporal Graph Signal Decomposition [PDF]
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021 Temporal graph signals are multivariate time series with individual components associated with nodes of a fixed graph structure. Data of this kind arises in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism.
Maxwell McNeil +2 more
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Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
IEEE Access, 2021 Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis.
Haiyang Pan +3 more
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Remote Sensing, 2022 Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal.
Zhiyuan Li +5 more
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Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications
Sensors, 2021 In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper.
Saiqiang Xia +5 more
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