Performance Analysis of Wavelet-based On-line Signal Detectors

Signal Analysis based on Complex Wavelet Signs

Article history: Received 10 December 2014 Received in revised form 5 August 2015 Accepted 12 August 2015 Available online 18 August 2015 Communicated by Radu Balan MSC: 42C40 94A12 44A15

Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...

A Performance Analysis of Adaptive Line Enhancer-Augmented Spectral Detectors

Analysis of Signal Denoising Methods Based on Wavelet Transform

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of ...

Wavelet-Based Algorithm for Signal Analysis

This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using waveletbased transform. The continuous wavelet transform (CWT) using the complex Morlet wavelet (CMW) is adopted to detect the harmonics presented in a power signal. A frequency detection algorithm is developed from the wavelet scalogram and ridges. A necessary condition is es...