Signal decomposition methods like Wavelet Transform (WT) have been widely used in a large set of applications such as image, speech and biomedical signal processing, reporting good results. In this paper, two versions of the WT are analyzed: the Discrete Wavelet Transform (DWT) and the Wavelet Packets (WP). The DWT is implemented by means of a filter bank with a fixed structure that provides a ...

OBJECTIVE To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP). METHODS A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms...

This paper proposes an approach for the protection of transmission lines with FACTS based on Artificial Neural Networks (ANN) using Wavelet Transform (WT). The required features for the proposed algorithm are extracted from the measured transient current and voltage waveforms using discrete wavelet transform (DWT). Those features are employed for fault detection and faulted phase selection usin...

This work is based on the comparative study of different decomposition methods used to de-noise an ECG signal. There are several signal processing techniques available like Fourier Transform method, Short Term Fourier Transform method, Wavelet analysis, Empirical Mode of Decomposition method etc. Though Fourier Transform method is predominantly used for decomposition purpose but it is not suita...

The quaternion Fourier transform (QFT), which is a nontrivial generalization of the real and complex Fourier transform (FT) using quaternion algebra has been of interest to researchers for some years (see e.g. [3, 5]). It was found that many FT properties still hold but others have to be modified. Based on the (right-sided) QFT, one can extend the classical wavelet transform (WT) to quaternion ...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

5 Methods for Non-Stationary Fractal Time-Series Analysis 15 5.1 Wavelet Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.2 Discrete Wavelet Transform (WT) Approach . . . . . . . . . . 16 5.3 Detrended Fluctuation Analysis (DFA) . . . . . . . . . . . . . 16 5.4 Detection of Trends and Crossovers with DFA . . . . . . . . . 19 5.5 Sign and Magnitude (Volatility) DFA . . . . . . . ....

In this paper we introduce the continuous quaternion wavelet transform (CQWT). We express the admissibility condition in terms of the (right-sided) quaternion Fourier transform. We show that its fundamental properties, such as inner product, norm relation, and inversion formula, can be established whenever the quaternion wavelets satisfy a particular admissibility condition. We present several ...

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are ne...

The Fourier transform usually has been used in the past for analysis of stationary and periodic signals. Its interest is the knowledge of spectral components existing in a waveform; it doesn't matter the moment where they happen. However, when the time localization of the spectral components is needed, the Wavelet Transform (WT) can be used to obtain the optimal timefrequency representation of ...