Abstract
In the present paper we define and study a new wavelet transformation associated to the linear canonical Dunkl transform (LCDT), which has been widely used in signal processing and other related fields. Then we define and study a class of pseudo-differential operators known as time-frequency (or localization) operators and we give criteria for its boundedness and Schatten class properties.
MSC:
47G10; 42B10; 47G30
1. Introduction and Preliminaries
The fields of special functions with reflection symmetries and harmonic analysis associated with root systems have seen particularly rapid progress in recent years. The theory of Riemannian symmetric spaces, whose spherical functions may be expressed as multivariate special functions based on certain discrete sets of parameters, provides some motivation for this topic.
Dunkl operators are a crucial tool in the study of special functions with reflection symmetries, which have generated considerable interest in mathematical physics, particularly in conformal field theory. To be more precise, we will fix some notation related to the Dunkl transform. For more details about Dunkl theory, we refer the reader to [1,2,3,4,5].
Let be the reflection in some hyperplane of orthogonal to and let be a root system such that , for every . Recall that the reflections associated to the root system generate a finite group , called the reflection group. For a multiplicity function k defined on , we introduce the index
and the weight function
where is a positive root system related to . Furthermore, we define the Mehta type constant
where is the usual norm on .
The Dunkl operators are defined by
for any orthonormal basis of .
The Dunkl kernel is the unique analytic solution on of the system
where . It satisfies
Moreover, it has a unique holomorphic extension to . In particular
for every and .
For , we define as the space of measurable functions f on such that
where In particular the Hilbert space is equipped with the scalar product
For a function , the Dunkl transform is given by
This transformation has recently become an interesting topic in harmonic analysis [6,7,8,9,10,11,12,13,14,15,16,17,18,19].
The Dunkl translation operator [4] is defined on by
This operator has an extension on the spaces , and , . Moreover, if , there is also an extension of the Dunkl translation operator on [5,6,20].
For a matrix in , such that , we define the linear canonical Dunkl transform (LCDT) of a function by
where
This transformation is an extension of the classical linear canonical transform (LCT), which was introduced independently by Collins [21] in paraxial optics and Moshinsky-Quesne [22] in quantum mechanics. The LCT is a flexible tool for investigating deep problems in signal processing, optics, and quantum physics [23,24,25,26,27,28] and so on. During the last years, the LCT has attracted a great interest and has been extended to a large class of integral transformations, see for example [29,30,31,32,33,34,35,36,37] and the references therein.
The generalized translation operator in the LCDT setting [38] is given by
Moreover, for every
where is the inverse of the matrix M.
In [39], we have introduced a new Gabor-type transformation associated with the LCDT, in order to concentrate signals in the time-frequency plane, but this concentration is restricted by the uncertainty principles.
It is well-known that wavelet theory is superior to Gabor theory in the localization of signals, because of its ability to measure the time-frequency variations of a signal at different time-frequency resolutions. It is often seen as an alternative to time-frequency analysis, and it has many roots and has become an interdisciplinary field combining applied mathematics [40], harmonic analysis [41,42], and signal and data processing [43].
To overcome the lack of localization in the LCDT setting, we will define in this paper a new wavelet-type transformation (see Section 3 for details), and then investigate its related localization operators (see Section 4 for details). This study extends the results proved in the recent paper [44] to the multidimensional case.
To be more precise, if (or , ) is an admissible Dunkl linear canonical wavelet, satisfying
where is the subspace of radial functions in . Then we define the family by
where
Therefore, the Dunkl linear canonical wavelet transform (DLCWT) is defined on by
For , the time-frequency localization operator associated to the DLCWT is defined on by
where is a measurable function defined on (called symbol), are two suitable functions on and is the weight measure given by .
We will prove that the DLCWT satisfies the following orthogonality relation: For all ,
In particular we deduce the Plancherel-type formula: For all ,
More generally we have for all and ,
Moreover, we will prove the following inversion formula: For every ,
Finally, for the DLCWT, we prove the following Calderón-type Reproducing Formula: If is an -function, where its LCDT is in and satisfies (13), then for every and , the function
belongs to , such that
where the function is defined by
Daubechies [45] and Ramanathan–Topiwala [46] were the first to introduce and study time-frequency localization operators, then this notion was extended and generalized by many researchers in different settings [47,48,49,50,51,52]. Components of a signal can be located and extracted using these operators from its representation in the time-frequency plane [53]. They have been used in physics as anti-Wick operators, which are tools for quantization processes [54] and in the approximation of pseudo-differential operators [55].
In this paper, we will study the boundedness and compactness of localization operators . In particular, we will prove that for any symbol , , are bounded, with
and belong to the Schatten class , such that
2. The LCDT and Its Properties
Throughout this paper is a matrix in , such that Notice that its inverse is given by and belongs to .
2.1. Linear Canonical Dunkl Transform (LCDT)
The LCDT was first defined and studied in [29], then generalized and improved in [38].
Definition 1.
The LCDT of any function is given by
where
We denote by the differential-difference operator given by
where and is the Dunkl-Laplacian operator.
Proposition 1.
Let .
- 1.
- and are connected by
- 2.
- We have
- 3.
- For every , the kernel satisfies
- 4.
- For everyand
- 5.
- For every
2.1.1. Specific Examples [29]
- 1.
- Let . If , then is the Fresnel transformation for the following Dunkl-type transformation:where
- 2.
- If , then is exactly the Dunkl transformation.
- 3.
- If , then is equal to the following transformation:where
- 4.
- If , then is the fractional Dunkl-type transformHere , where
2.1.2. LCDT on ,
We define the two operators and , by
Then we have the following properties on :
- 1.
- We have
- 2.
- We have
Theorem 1
(Riemann–Lebesgue-type Inequality). For every , its is in such that
Proposition 2
(Plancherel-type Formulas).
- 1.
- For every , we have
- 2.
- If u belongs to , then belongs to such that
- 3.
- The LCDT has a unique extension to an isometric isomorphism on , which is still denoted by .
- 4.
- For any ,
- 5.
- For every with
Definition 2.
For , the LCDT is defined on by
where is the Dunkl transform on .
Proposition 3.
For , the LCDT extends to a linear bounded operator on withg
The last inequality is a Young-type relation for the Dunkl transform.
2.2. Generalized Convolution Product for the LCDT
In this paragraph, we define the convolution product associated with the LCDT and give its properties, see [38].
Definition 3.
The generalized translation operator associated with the operator is given by:
Then we have the following properties.
Proposition 4.
Let .
- 1.
- and
- 2.
- The translation product formula is given by:
- 3.
- The translation operator is continuous from into itself, from into itself, and on . That is, for , we haveMoreover, if , then
- 4.
- If , then for any ,
- 5.
- For all (resp. ), we have
- 6.
- For every
Corollary 1.
For every and we have:
Definition 4.
The generalized convolution product associated with of two suitable functions u and v on , is the function defined by:
Then we have the following properties:
- 1.
- 2.
Proposition 5
(Young-type Relation). Let such that If , then for every and , we have and
In addition, if and , then
Proposition 6.
- 1.
- For and , we have
- 2.
- If and , then
- 3.
- For , we have
- 4.
- It is not necessary for the functions to be radials for the previous three results to hold if .
- 5.
- For all , we have
3. The DLCWT
We denote by and , denotes the space of functions on such that
where .
In this section, we will introduce the generalized linear canonical wavelet transform associated with the operator , and we give some of its properties.
Definition 5.
Let or , . We say that φ is an admissible Dunkl linear canonical wavelet if for almost
Notice that, in the case of the reflection group , we can take in the previous definition , .
Example 1.
The function , defined on by
satisfies
The function is an admissible Dunkl linear canonical wavelet on and we have .
For , and a suitable function, we define the family by
where
Proposition 7.
Let and .
- 1.
- For all ,
- 2.
- For every ,
- 3.
- For all
- 4.
- For all ,
- 5.
- If , then for all ,
Proof.
Definition 6.
Let φ be an admissible Dunkl linear canonical wavelet. Then the Dunkl linear canonical wavelet transform (DLCWT) of any regular function is denoted by and is defined as
where is given by (61).
The last formula can also be written as
The DLCWT satisfies the following properties:
- 1.
- 2.
- 3.
- If , as above for any and for all
- 4.
In the remainder of this section, will be an admissible linear canonical Dunkl wavelet.
Theorem 2
(Plancherel-type formula). For all , we have
Proof.
Involving (70), (74), and the Riesz–Thorin interpolation theorem, we derive the following proposition.
Proposition 8.
For and , we have
Theorem 3
(Orthogonality Property). For all we have
Proof.
Plancherel-type formula for DLCWT implies that for all , the function
is in . Consequently, for almost all . In regard to convolution and Parseval’s formula applied to the variable t, we obtain
In view of Fubini’s theorem, we can justify the interchange of the integral
The conclusion follows due to Parseval’s formula for LCDT. □
Now will provide a weak inversion formula for the DLCWT.
Theorem 4
(Inversion Formula). The inversion formula states
Proof.
Let be an admissible linear canonical Dunkl wavelet and . We have
Then the result follows. □
Equation (59) can be generalized as follows.
Definition 7.
Let u and v be in . We say that the pair is an admissible Dunkl linear canonical two-wavelets on if
is constant for almost every .
Theorem 5.
Let be an admissible Dunkl linear canonical two-wavelets. Then, for any , we have
Proof.
Theorem 6
(Calderón’s Reproducing Formula). Let be an admissible Dunkl linear canonical wavelet such that belongs to . Then, for any f in and , the function
belongs to , and satisfies
where the function is defined by
The following lemmas are required in order to prove Theorem 6.
Lemma 1.
Let be the function defined by
then under the assumptions of Theorem 6, we have
and
Proof.
As is an admissible Dunkl linear canonical wavelet, then from (59)
Therefore
Finally, using the fact that
we derive the result. □
Lemma 2.
The function defined by (80) belongs to and satisfies
Proof.
According to (52) and (69), the function can also be stated as
Then
Therefore
From Plancherel’s formula (40), (56) and (58), we deduce that
Using (59) and (38) we have
Therefore
Additionally (40) gives
Thus, we conclude that
Proof of Theorem 6.
Proposition 9
(An Inversion Formula for ). For all (resp. ) such that (resp. ),
where the function is defined by
Proof.
Let
and let
We will prove (93), in the case of and . Using Parseval’s formula, Equations (39), (50) and (56), we have
Then for all
Thus, by Fubini’s theorem, we obtain
Involving the inversion Formula (42), we obtain the desired relation.
On the other hand. if and , we have belongs to and
Next, using a similar argument as in the first case, we obtain the result. □
4. Localization Operators (LO) for the DLCWT
Let be a compact operator. Then its singular values are the eigenvalues of the positive self-adjoint operator .
The set of all compact operators whose singular values are in is known as the Schatten class , , and are equipped with the norm
We define , with its norm,
If K is an operator in , then its trace is defined by
for any orthonormal basis of and if K is nonnegative then
Furthermore, if the positive operator is in , for a compact operator , then K is Hilbert–Schmidt, such that
for any orthonormal basis of .
Definition 8.
Let ϱ be a measurable function on Ω and let be two admissible Dunkl linear canonical wavelets. Then for , the (two-wavelet) LO associated to the DLCWT is defined on by
The definition of (103) is frequently easier to understand in a weak sense: More precisely for , if and ,
For , the adjoint of is , that is,
In this section, u and v are two admissible Dunkl linear canonical wavelets in such that
4.1. Boundedness of LO
In this subsection we will prove that, for all , , the LO is bounded. To do this, we first consider the problem for and then for , and the conclusion follows by interpolation theory.
Proposition 10.
For , the LO , such that
Proposition 11.
For , the LO , such that
Proof.
Consequently for each , , the LO is well defined and belongs to .
Theorem 7.
Let and let . Then there is a unique operator in , satisfying
Proof.
For let the function defined by Then, from the previous two propositions, we have
and
Thus, using interpolation theory (greensee ([56], Theorem 2.11), may be uniquely extended to an operator on , with
As desired. □
4.2. Schatten Class Properties of LO
In this subsection we will show that the LO
belongs to the Schatten class .
Proposition 12.
Let ϱ be in . Then the localization operator is in and we have
Proof.
Proposition 13.
Let . If ϱ is in then is compact.
Proof.
Assume that . Let be a sequence in such that in . Then from Theorem 7
Therefore in as . Moreover, since is in hence compact, it follows that is compact. □
Theorem 8.
If , then belongs to , such that
where is given by
Proof.
Since is in , by Proposition 12, is in , then from the canonical form for compact operators given in ([56], Theorem 2.2), there is an orthonormal basis for the orthogonal complement of the kernel of the operator , consisting of eigenvectors of and an orthonormal set in , satisfying
where are the nonnegative singular values of related to . Then
Therefore from Bessel inequality and Equations (63) and (68), we have
Thus
Since , then by (115), we have
Then, from Fubini’s theorem, we obtain
Thus □
Corollary 2.
For ϱ in , we have the following trace formula
Proof.
Given an orthonormal basis of . We have from Theorem 8, that belongs to . Therefore by (100) and Parseval’s relation,
□
Corollary 3.
Let . If , then belongs to , such that
Proof.
This is the consequence of interpolating (see ([56], Theorem 2.10 and Theorem 2.11)), Proposition 11, and Theorem 8. □
Remark 1.
If is positive and real valued and if , then
is nonnegative. Thus from Corollary 2 and Equation (101),
Now we state a result concerning the trace of products of localization operators.
Corollary 4.
Let and be any real-valued and non-negative functions in . We assume that such that . Then the localization operators and are positive and trace class operators, such that
for any natural number n.
Proof.
By Theorem 1 in the paper [57], we know that if the operators A and B are in the trace class and are positive, then
Hence, if we take and , then the result follows by Remark 1. □
5. -Boundedness and -Compactness of LO
For , let and be two admissible Dunkl linear canonical wavelets.
5.1. Boundedness
In this subsection we will prove that is bounded from to itself.
Proposition 14.
Let and . For , the localization operator is bounded, with
Proposition 15.
Let and . Then for , the localization operator is bounded and we have
Remark 2.
Proposition 15 is also a corollary of Proposition 14, since is the adjoint of .
Using an interpolation of Propositions 14 and 15, we obtain the following result.
Theorem 9.
Let u and v be functions in . Then for all ϱ in , there exists a unique linear bounded operator , satisfying
Another variant of the -boundedness can be provided. To do this, we start by improving Proposition 15.
Proposition 16.
Let and , . Then for , the localization operator is bounded such that
Proof.
The following result is obtained by using the Propositions 14 and 16.
Theorem 10.
Let and let , . Then for , the LO is bounded, such that
With Schur’s technique, we can obtain an -boundedness result as in the previous Theorem, but the estimate for the norm is weaker than that of (124).
Proposition 17.
Let ϱ be in and let . Then there exists a unique linear bounded operator , such that
Proof.
Define on by
Then we have
By simple calculations, it is easy to see that
and
Thus by Schur’s Lemma [58], the linear operator is bounded for any , and we have
□
Theorem 11.
Let ϱ be in , , and let be two admissible linear canonical wavelets. Then for all , there is a unique linear bounded operator satisfying
where
and
Proof.
Let defined by . Then from Theorem 7 and Proposition 14, we obtain
and
Therefore, by (127), (128) and the the multi-linear interpolation theory (see Section 10.1 in [59]), we obtain a unique linear bounded operator
that satisfies
where
and
By the definition of , we have
Since is the adjoint of , then the linear operator is bounded on such that
where
Using an interpolation of (130) and (131), we have for any ,
with □
Theorem 12.
Let , , and let . Then there exists a unique linear bounded operator , , such that
where
and
To prove this theorem, we need the following lemmas.
Lemma 3.
Let , and let , . Then there is a unique linear bounded operator satisfying
Proof.
Lemma 4.
Let , and let , . Then there is a unique operator satisfying
Proof.
Since is the adjoint of , then we have the desired result by using Lemma 3 and a duality argument. □
Proposition 18.
Let be such that . Let and let , . Then there exists a unique operator with
where
and
Proof.
The proof follows from Theorem 10 and Theorem 7 with , q instead of p, and interpolation theory. □
5.2. Compactness of for Symbols in
Proposition 19.
Under hypothesis of the Theorem 11, the LO is compact.
Proof.
Given a sequence that satisfies weakly in as . It is enough to show that Since
then using the fact that weakly in , we deduce that
However, since weakly in , there is such that . Therefore, using (66) and a basic computations, we obtain for all and ,
Moreover, from Fubini’s theorem and (66), we have
Thus, from the (138)–(141) we deduce that . □
Theorem 13.
Let and , . Then for , the operator is compact.
Proof.
From the previous proposition, we only need to show that the conclusion holds for . In fact, the operator is the adjoint of , which is compact by the previous proposition. Then by the duality property is compact. Finally, by an interpolation of the compactness on and on , such as the one given on pages 202 and 203 of the book [60], we obtain the result. □
Theorem 14.
In accordance with the assumptions of Theorem 11, the LO is compact for every .
Proof.
This is a direct result by interpolating Proposition 19 and Corollary 3, (see ([60], pages 202 and 203)). □
Remark 3.
Notice that, in the case of the reflection group , all results of this section remain true for the admissible Dunkl linear canonical wavelets , .
5.3. Examples
As per Wong’s perspective in his work [61], we will give some examples of localization operators.
- 1.
- Linear canonical Dunkl multiplier (LCDM): For , we define the linear operatorbyIt is called the linear canonical Dunkl multiplier with symbol m. We will show that if the symbol is a function that depends only on the second variable, then is an LCDM.Proposition 20.Suppose that ϱ is a function defined on Ω bywhere σ is a function defined on . Then where is the LCDM associated to the symbolProof.For all , let defined bywhereOn the other hand, by simple calculations, we obtainin and almost everywhere on as . Then by (143) we obtainas . Therefore for every ,Thuswhere is the LCDM, such that m is defined by□
- 2.
- Paraproduct: Now we will assume that depends only on the second variable, that is,where is suitable function on . Using Parseval’s formula (41) and Fubini’s theorem, we obtain for allMoreover, Fubini’s theorem giveswhereFrom (145) and since is in , then is bounded such thatWe can therefore provide an -estimate on the paraproduct .Lemma 5.For all , we haveProposition 21.For every we haveProof.From Proposition 11,Therefore using (145) we deduce thatIt follows from the Hahn–Banach theorem that is in the dual of andThe proof is complete. □
6. Conclusions and Perspectives
Using the harmonic analysis of the LCDT, we have introduced and studied a new type of wavelet transformation (called DLCWT), by proving all of its known properties, such that a Plancherel-type formula, an orthogonality relation, an inversion and a Calderón-type Reproducing formulas. Then we define the notion of localization operators associated with the DLCWT. For these operators, we studied the boundedness, compactness, and Schatten–von Neumann class properties.
It is well-known that the uncertainty principles set a limit to the simultaneous localization of a signal and its LCDT. In future work, we will study concentrations operators, which are a special case of localization operators presented in this paper, to measure the time-frequency content of the -functions on some subset of finite measure. In particular, we will define and study the scalogram associated with the DCLWT, and show that the signal space spanned by the first eigenfunctions of the concentration operator has the maximum localized scalogram in the region of interest in the time-frequency plane. Then we will show that any essentially concentrated function (functions that are almost concentrated on a subset of finite measure) can be approximated by a linear combination of such eigenfunctions, and corresponding error estimates can be given.
Author Contributions
Conceptualization, S.G.; Methodology, H.M.; Validation, S.G.; Formal analysis, H.M.; Investigation, H.M.; Writing—original draft, H.M.; Writing—review and editing, S.G.; Visualization, H.M.; Project administration, S.G.; Funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU252041].
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Acknowledgments
The authors thank the anonymous referees for their helpful comments and suggestions that helped improve the text of this manuscript. The second author is deeply indebted to Khalifa Trimèche and Man Wah Wong for their helps.
Conflicts of Interest
The authors declare no conflicts of interest.
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