Improved LPA-ICI-based estimators embedded in a signal denoising virtual instrument
Sign Up to like & getrecommendations! Published in 2017 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-016-0921-6
Abstract: Adaptive filters embedded in a user-friendly virtual instrument (VI) for signal denoising are proposed in the paper. The filters are based on the local polynomial approximation (LPA) and equipped with the improved intersection of confidence… read more here.
Keywords: virtual instrument; signal denoising; improved lpa; lpa ici ... See more keywords
Signal denoising of viral particle in wide-field photon scattering parametric images using deep learning
Sign Up to like & getrecommendations! Published in 2022 at "Optics Communications"
DOI: 10.1016/j.optcom.2021.127463
Abstract: Abstract Polarization parametric indirect microscopic imaging (PIMI) can obtain anisotropic nanoscale structural information of the sample by utilizing a polarization modulated illumination scheme and fitting the far-field variation of polarization states of the scattered photons.… read more here.
Keywords: viral particle; field; denoising viral; pimi ... See more keywords
A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery
Sign Up to like & getrecommendations! Published in 2017 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2017.02.036
Abstract: Abstract Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular… read more here.
Keywords: fault feature; feature; weak fault; signal denoising ... See more keywords
Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment
Sign Up to like & getrecommendations! Published in 2018 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/aaa50a
Abstract: Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based… read more here.
Keywords: fault; frequency; multisensor signal; wavelet transform ... See more keywords
Signal denoising through topographic modularity of neural circuits
Sign Up to like & getrecommendations! Published in 2022 at "eLife"
DOI: 10.1101/2022.01.10.475681
Abstract: Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout… read more here.
Keywords: modularity neural; denoising topographic; signal denoising; modularity ... See more keywords
EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2019.2962658
Abstract: Background. The most common and successful technique for signal denoising with non-stationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of… read more here.
Keywords: eeg signals; mean square; signal denoising; wavelet transform ... See more keywords
A Joint Framework for Seismic Signal Denoising Using Total Generalized Variation and Shearlet Transform
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3049644
Abstract: Seismic exploration is a remote-sensing tool applied in a great many projects for engineering and resource-exploration purposes. Random noise suppression is one of the key steps in seismic-signal processing, especially those with important details and… read more here.
Keywords: shearlet; shearlet transform; seismic signal; signal denoising ... See more keywords
Signal Denoising and Detection for Uplink in LoRa Networks Based on Bayesian-Optimized Deep Neural Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3217337
Abstract: Long-range and low-power communications are suitable technologies for the Internet of things networks. The long-range implies a very low signal-to-noise ratio at the receiver. In addition, low power consumption requires reduced signaling, hence the use… read more here.
Keywords: networks based; long range; signal denoising; detection ... See more keywords
Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2019.2932715
Abstract: A novel multivariate signal denoising method is presented that computes Mahalanobis distance measure at multiple data scales obtained from multivariate empirical mode decomposition (MEMD) algorithm. That enables joint multichannel data denoising directly in multidimensional space… read more here.
Keywords: multivariate signal; tex math; distance; signal denoising ... See more keywords
Positive Sparse Signal Denoising: What Does a CNN Learn?
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3160372
Abstract: Convolutional neural networks (CNNs) provide impressive empirical success in various tasks; however, their inner workings generally lack interpretability. In this paper, we interpret shallow CNNs that we have trained for the task of positive sparse… read more here.
Keywords: sparse signal; signal denoising; denoising cnn; positive sparse ... See more keywords
Motion Artifacts Suppression From EEG Signals Using an Adaptive Signal Denoising Method
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3142037
Abstract: Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be… read more here.
Keywords: signal denoising; motion; signal; motion artifacts ... See more keywords