Let G = ( R∗+ × SO0(1, n) ) n Rn+1 be the Weyl-Poincaré group and KAN be the Iwasawa decomposition of SO0(1, n) with K = SO(n). Then the “affine Weyl-Poincaré group” Ga = ( R∗+ × AN ) n Rn+1 can be realized as the complex tube domain Π = Rn+1 + iC or the classical Cartan domain BDI(q = 2). The square-integrable representations of G and Ga give the admissible wavelets and wavelet transforms. An ...

Signal de-noising is one of the classical problems in the field of signal processing. As a new signal processing tools, wavelet analysis, which has excellent noise performance, has caused growing concern and attention. The wavelet threshold de-noising has been researched systematacially, and the wavelet de-noising method is used on the GPS signal, which has achieved very good results.

The paper presents computations of decaying two{dimensional turbulence in an adaptive wavelet basis. At each time step the vorticity is represented by an adaptively selected set of wavelet functions which adjusts to the instantaneous distribution of vorticity. The results of this new algorithm are compared to a classical Fourier method and a Fourier method supplemented with wavelet compression ...

We discuss features extracted from a wavelet packet decomposition for image classification. Statistical features computed from wavelet packet coefficients are compared to structural features which are derived from an image dependent wavelet packet decomposition subband structure. Primary application area is the classification of pit pattern structures in zoom-endoscopic colon imagery, while res...

This study compares the proposed hybrid method (wavelet robust M-estimation) to traditional ordinary least square) when there are de-noising or outlier problems for estimating multiple linear regression models using statistical criterion root mean square error (RMSE). According simulated and real data, is better than classical (Wavelet Ordinary Least Square) more accurate. The of less Wavelet S...

Increasing requirements for the technical condition of machines induce the development of novel diagnostic methods for possible fault detection and identification in an early phase. Most of these methods are based on the processing of vibration signals. The classical methods often do not give full information about the actual condition of a machine. Therefore, it is necessary developing the app...

Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the di ering associations between those predictors and responses. The classical finite mixture regression model is extended to incorporate functional predictors by taking a wavelet-based approach in which both the functional predictors...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

Classical discrete wavelet packet transforms are sensitive to changes in image orientation and translation. Therefore, it is hardly possible to extract rotation invariant features from images in the transform domain. This paper proposes several algorithms for invariant discrete wavelet decomposition to produce an invariant representation for an image. The procedure can be divided into several s...