Paper Info
Title
VHDL Modelling of Fixed-point DWT for the Purpose of EMG Signal Denoising
Abstract
Wavelet Transform (WT) has been widely applied in biomedical signal analysis. This paper will present the denoising method of EMG signal using WT and its model processed by VHSIC (Very High Speed Integrated Circuit) Hardware Description Language (VHDL) model of it. The principle of wavelet denoising is first to decompose the signal by performing a WT, followed by applying suitable thresholds to the detail coefficients, zeroing all coefficients below their associated thresholds, and finally to reconstruct the denoised signal based on the modified detail coefficients. Discrete Wavelet Transform (DWT) is a method that uses wavelet analyzer in which case the signal split into small pieces preserving both time and frequency properties. The Second order of Daubechies family (db2) has been used to denoise EMG signals. The simulation, synthesis and verification of the design presents a fast and reliable prototyping of DWT for denoising of EMG signals.
Year
DOI
Venue
2011
10.1109/CICSyN.2011.58
Computational Intelligence, Communication Systems and Networks
Keywords
Field
DocType
emg signal denoising,denoised signal,wavelet transform,denoising method,signal split,modified detail coefficient,vhdl modelling,fixed-point dwt,discrete wavelet transform,emg signal,detail coefficient,wavelet denoising,biomedical signal analysis,fixed point,noise,dwt,software reliability,hardware description languages,vhdl,signal reconstruction,solid modeling,integrated circuit
Signal processing,Computer science,Artificial intelligence,Discrete wavelet transform,Wavelet,Wavelet transform,Mathematical optimization,Pattern recognition,Second-generation wavelet transform,Speech recognition,VHDL,Stationary wavelet transform,Signal reconstruction
Conference
ISBN
Citations 
PageRank 
978-0-7695-4482-3
1
0.37
References 
Authors
1
3
Name
Order
Citations
PageRank
Md. R. Ahsan142.59
muhammad ibn ibrahimy241.44
Othman O. Khalifa3153.53