Adaptive filter support selection for signal denoising based on the improved ICI rule

2013 ◽  
Vol 23 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Victor Sucic ◽  
Jonatan Lerga ◽  
Miroslav Vrankic
Keyword(s):  
Adaptive Filter ◽  
Signal Denoising ◽  
Selection For

Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

2011 ◽  
Vol 121-126 ◽  
pp. 4259-4264 ◽  
Author(s):  
Jian Hui Wang ◽  
Na Chen ◽  
Qian Xiao ◽  
Jian You Xu ◽  
Shu Sheng Gu
Keyword(s):  
Neural Network ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Signal Denoising ◽  
Semg Signal ◽  
Filter Model ◽  
The Neural Network ◽  
Filtering Effect

For the large computation workload of the adaptive filter algorithm and the low filtering speed of the adaptive filter model based on wavelet transform, a wavelet-based neural network adaptive filter model is constructed in this paper. As the neural network has the capacity of distributed storage and fast self-evolution, Hopfield neural network is used to implement adaptive filtering algorithm LMS, so as to increase the computing speed. The model applied to sEMG signal denoising can achieve a better filtering effect.

scholarly journals Bayesian Selection for the $\ell _2$ -Potts Model Regularization Parameter: 1-D Piecewise Constant Signal Denoising

2017 ◽  
Vol 65 (19) ◽  
pp. 5215-5224 ◽  
Author(s):  
Jordan Frecon ◽  
Nelly Pustelnik ◽  
Nicolas Dobigeon ◽  
Herwig Wendt ◽  
Patrice Abry
Keyword(s):  
Potts Model ◽  
Regularization Parameter ◽  
Signal Denoising ◽  
Piecewise Constant ◽  
Selection For

Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising

2021 ◽  
Vol 63 ◽  
pp. 102221
Author(s):  
Mahesh Chandra ◽  
Pankaj Goel ◽  
Ankita Anand ◽  
Asutosh Kar
Keyword(s):  
Adaptive Filter ◽  
High Speed ◽  
Signal Denoising ◽  
Ecg Signal

Study and Improving on Adaptive Filter

2011 ◽  
Vol 121-126 ◽  
pp. 1392-1396
Author(s):  
Hong Xia Pan ◽  
Ying Ying Zhang
Keyword(s):  
Adaptive Filter ◽  
Vibration Signal ◽  
Lms Algorithm ◽  
Signal Denoising ◽  
Least Mean Square ◽  
Theoretical Significance ◽  
Fast Convergence Rate ◽  
Tangent Function ◽  
Gearbox Vibration ◽  
Fault Feature Extraction

In this paper the principle of adaptive filter and various least mean square (LMS) adaptive filter algorithm is studied, based on the related hyperbolic tangent function LMS algorithm is presented, referred to as CTanh-LMS algorithm. Simulation results show that, compared with other adaptive filter algorithm, this method has better denoising ability, and the algorithm is simple, fast convergence rate, and can satisfy the gearbox vibration signal denoising requirements. The proposed algorithm can not only solve the gearbox fault feature extraction, and give adaptive filter algorithm research provides a new means, has important theoretical significance and practical value.

scholarly journals Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

2021 ◽  
Vol 27 (3) ◽  
pp. 799-815
Author(s):  
Hassan Ashraf ◽  
Asim Waris ◽  
Syed Omer Gilani ◽  
Muhammad Umair Tariq ◽  
Hani Alquhayz
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Denoising ◽  
Emg Signal ◽  
Mode Decomposition ◽  
Parameters Selection ◽  
Selection For

Adaptive filter length selection for acoustic echo cancellation

2009 ◽  
Vol 89 (6) ◽  
pp. 1185-1194 ◽  
Author(s):  
Christian Schüldt ◽  
Fredric Lindstrom ◽  
Haibo Li ◽  
Ingvar Claesson
Keyword(s):  
Adaptive Filter ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Filter Length ◽  
Acoustic Echo ◽  
Selection For

Framework for Selecting Vocabulary for Preliterate Children Who Use Augmentative and Alternative Communication

2019 ◽  
Vol 28 (3) ◽  
pp. 1000-1009
Author(s):  
Allison Bean ◽  
Lindsey Paden Cargill ◽  
Samantha Lyle
Keyword(s):  
Language Impairment ◽  
Augmentative And Alternative Communication ◽  
Developmentally Appropriate ◽  
School Age Children ◽  
Alternative Communication ◽  
School Based ◽  
Selection For ◽  
Nonverbal Children ◽  
Grammatical Structures ◽  
Insufficient Knowledge

Purpose Nearly 50% of school-based speech-language pathologists (SLPs) provide services to school-age children who use augmentative and alternative communication (AAC). However, many SLPs report having insufficient knowledge in the area of AAC implementation. The objective of this tutorial is to provide clinicians with a framework for supporting 1 area of AAC implementation: vocabulary selection for preliterate children who use AAC. Method This tutorial focuses on 4 variables that clinicians should consider when selecting vocabulary: (a) contexts/environments where the vocabulary can be used, (b) time span during which the vocabulary will be relevant, (c) whether the vocabulary can elicit and maintain interactions with other people, and (d) whether the vocabulary will facilitate developmentally appropriate grammatical structures. This tutorial focuses on the role that these variables play in language development in verbal children with typical development, verbal children with language impairment, and nonverbal children who use AAC. Results Use of the 4 variables highlighted above may help practicing SLPs select vocabulary that will best facilitate language acquisition in preliterate children who use AAC.

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