New classes of frame wavelets for applications

For many applications, general frame wavelets are more attractive than orthonormal wavelets owing to the extent to which the former may be tailored to suit the application. The mother wavelet may be chosen to cause, during wavelet transform, congealing of selected features into localized regions in the wavelet domain or separation of chosen features from one another during transform. Thus choice of mother wavelet impacts feature dimensionality as well as separability among features. Owing to the special importance of frames, there is a need to identify and characterize new frame wavelets in order to make available a rich variety of mothers which run the gamut of the diverse applications. This paper discusses three classes of frame wavelet mothers, to include new mothers as well as a few familiar ones, approached from the viewpoints of residues, sigmoid, and Hermitivity.

[1]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[2]  B. Irie,et al.  Capabilities of three-layered perceptrons , 1988, IEEE 1988 International Conference on Neural Networks.

[3]  Yagyensh C. Pati,et al.  Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations , 1993, IEEE Trans. Neural Networks.

[4]  A. Grossmann,et al.  DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .

[5]  Hui Xie,et al.  Design of orthonormal wavelets with better time-frequency resolution , 1994, Defense, Security, and Sensing.

[6]  R. Hecht-Nielsen,et al.  Theory of the Back Propagation Neural Network , 1989 .

[7]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[8]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[9]  A. Cohen Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, I. Daubechies, SIAM, 1992, xix + 357 pp. , 1994 .

[10]  L. G. Weiss Wavelets and wideband correlation processing , 1994, IEEE Signal Processing Magazine.

[11]  Deepen Sinha,et al.  On the optimal choice of a wavelet for signal representation , 1992, IEEE Trans. Inf. Theory.

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.