This paper presents a new signal processing technique for bearing defect detection, called Multi-Scale Enveloping Spectrogram (MUSENS). The technique decomposes vibration signals measured on rolling bearings into different scales by means of a continuous wavelet transform (CWT). The envelope signal in each scale is then calculated from the modulus of the wavelet coefficients. Subsequently, Four...

This paper presents a new denoising method in the wavelet domain, which aims to reduce the noise, preserving the structural features (like the discontinuities) and textural information of the scene. In this paper we propose the association of the Double Tree Complex Wavelet Transform, (DT-CWT) with a Maximum A Posteriori (MAP) filter named bishrink. The corresponding denoising algorithm is simp...

In the paper, we improve the Local Binary Pattern Histogram (LBPH) approach and combine it with Dual-Tree Complex Wavelet Transform (DT-CWT) to propose a Dual-Tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram (DT-CWT based LBPWH) method for palmprint representation and recognition. The approximate shift invariant property of the DT-CWT and its good directional selecti...

The continuous wavelet transform (CWT) can be seen as a filterbank having logarithmic frequency subbands spacing similar to the human auditory system. Thus, to make computers imitate the significant functions of the human auditory system, one promising approach would be to model, analyze and process magnitude spectrograms given by the CWT. To realize this approach, we must be able to convert a ...

We have developed the theory and analog hardware implement-ation for a Cont.inuous Wavelet Transform (CWT). This novel circuit performs an orthogonal decomposition of temporal signals, such as speech, in a frequency range of IOHz to 20KHz. This multiresolution processor expands the Laguerre structure originally developed by Lee [4].

The computation complexity dominates Continuous Wavelet Transform (CWT) and the low frequency resolution limit the Discrete Wavelet Transform (DWT). The degradation in frequency resolution in Discrete Wavelet Transform (DWT) is due to the fact, that, DWT decomposes only the approximation coefficients at each level. This detail is not enough to analyse the signal like knock in the engine. The ac...

We present a purely group-theoretical derivation of the continuous wavelet transform (CWT) on the 2-sphere S2, based on the construction of general coherent states associated to square integrable group representations. The parameter space X of our CWT is the product of SO(3) for motions and R+∗ for dilations on S2, which are embedded into the Lorentz group SO0(3,1) via the Iwasawa decomposition...

We show that the application of the discrete wavelet transform (DWT) using the Haar basis to the increments of fractional Brownian motion (f Bm), also known as discrete fractional Gaussian noise (DFGN), yields coefficients which are weakly correlated and have a variance that is exponentially related to scale. Similar results were derived by Flandrin, Tewfik, and Kim for a continuous-time fBm go...

Image segmentation is an important step in many computer vision algorithms. The objective of segmentation is to obtained an optimal region of convergences (ROC). Error in this stage will impact all higher level activities. This paper focuses on a new efficient method denoted as Complex-Valued Artificial Neural Network with Complex Wavelet Transform (CWT-CVANN) for the segmentation of lung regio...

We present a group-theoretical derivation of the continuous wavelet transform (CWT) on the 2-sphere S2, based on the construction of coherent states associated to square integrable group representations. The parameter space X is the product of SO(3) x J&, embedded into the Lorentz group S00(3, 1) via the Iwasawa decomposition, and X Y SO,(3,1)/C. The space L2(S2, a!~) carries a unitary irreduci...