Abstract
We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).
Highlights
See the paper submitted on 11 February 2016 to “General Relativity and Quantum Cosmology” [6]. These types of works on wavelets sparked the research on continuous wavelet transform of functions and generalized functions
My earlier work in this connection was on the generalized function space D 0 ( Rn ), n ≥ 1
In order to deal with the wavelet transform
Summary
(a) Wavelet analysis has entered into almost every walk of human life [1,2,3,4,5]. See the paper submitted on 11 February 2016 to “General Relativity and Quantum Cosmology” [6] These types of works on wavelets sparked the research on continuous wavelet transform of functions and generalized functions. My earlier work in this connection was on the generalized function space D 0 ( Rn ), n ≥ 1 The disadvantage in this space was that two functions having the same wavelet transform may differ by a constant even though all the moments of the wavelets are nonzero. All the even order moments of this wavelet are zero and so two functions having the same wavelet transform may differ by a polynomial. These two distributions have the same wavelet transform but they may differ by a polynomial involving a constant term.
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