Abstract
One of the best known time–frequency tools for examining non-transient signals is the linear canonical windowed transform, which has been used extensively in signal processing and related domains. In this paper, by involving the harmonic analysis for the linear canonical Dunkl transform, we introduce and then study the linear canonical Dunkl windowed transform (LCDWT). Given that localization operators are both theoretically and practically relevant, we will focus in this paper on a number of time–frequency analysis topics for the LCDWT, such as the boundedness and compactness of localization operators for the LCWGT. Then, we study their trace class characterization and show that they are in the Schatten–von Neumann classes. Then, we study their spectral properties in order to give some results on the spectrograms for the LCDWT.
Keywords:
Dunkl transform; linear canonical transform; generalized translation; generalized convolution; generalized windowed transform; localization operators; spectrogram MSC:
47G10; 42B10; 47G30
1. Introduction
Charles Dunkl [1] introduced and explored Dunkl operators as part of an effort to expand the classical theory of spherical harmonics. They are used in quantum mechanics to examine one-dimensional harmonic oscillators that are subject to Wigner’s commutation principles, in addition to their mathematical significance.
For every function , its Dunkl transform associated with a reflection group W and a non-negative multiplicity function k is given by
where is a weighted measure and represents the Dunkl kernel. The Dunkl transform offers a natural extension of the standard Fourier transform for and may be uniquely extended to an isometric isomorphism on . We direct the reader to [2,3,4] and the references therein for a thorough understanding of the Dunkl transform.
Numerous significant advancements in the field of phase-space analysis have previously been studied within the Dunkl transform. For instance, the authors in [5,6] developed many uncertainty inequalities for the Dunkl windowed transform. Furthermore, in [7,8], the authors examined several relations of quantitative uncertainty principles related to the continuous Dunkl wavelet transform.
The recent advancements in deformed windowed transforms have greatly inspired us to investigate a new windowed transform employing a pair of generalized translation and modulation linked to the linear canonical Dunkl transform found in [9].
A generic homogeneous linear lossless mapping in phase space is related with the classical linear canonical transform (LCT), which is an integral transform that is independent of phase space coordinates and depends on three parameters. It was separately developed to investigate the conservation of information and uncertainty under linear mappings of phase space by Collins [10] in paraxial optics and Moshinsky-Quesne [11] in quantum mechanics.
The three parameters constitute an augmented matrix consisting of a uni-modular matrix of . It maps the position x and the wave number y into
where
It is possible to convert any convex body into another convex body while preserving the body’s area. The homogeneous special group is composed of these modifications.
Given a real augmented matrix
Then the linear canonical transform of any function f with respect to M is defined by
where
The LCT is a generalized Fourier transform that is an extension of several well-known integral transformations, such as the Fourier transform, the Fresnel transform, the fractional Fourier transform, scaling operations, etc.
The LCT is more versatile than other transforms because of its additional degrees of freedom and straightforward geometrical representation, making it a useful and effective tool for examining deep issues in signal processing, quantum physics, and optics [12,13,14,15].
Recently, the LCT has gained considerable attention and has been extended to a wide class of integral transforms; see, for example [16,17,18,19,20], and the references therein.
Over the past few decades, the LCT’s application areas have grown rapidly, making it suitable for investigating complex problems in a variety of fields, including holographic three-dimensional television, quantum physics, pattern recognition, radar analysis, signal analysis, filter design, phase retrieval problems, and many other fields, such as uncertainty principles [21], Poisson summation formulae [22], convolution theorems [23], and sampling theorems [24].
The purpose of this document is threefold. First by using the main tools related to the linear canonical Dunkl transform (LCDT) [9], we present and study a new windowed-type transform, which we will call the linear canonical Dunkl windowed transform (LCDWT). Then, we will show the main theorems of the harmonic analysis of this transform. The windowed Fourier transformation has proven to be highly effective in a variety of physical and technical applications, such as quantum optics, signal processing, and wave propagation [25]. Moreover, it has seen several developments in recent years. For instance, we refer to [6,26,27,28,29,30] and the references therein.
The following are this article’s main contributions:
- -
- To derive a new inversion relation for the LCDWT.
- -
- To study the boundedness and compactness of the localization operators associated with the LCDWT in the Schatten classes.
- -
- To study the eigenvalues and eigenfunctions of the time–frequency Toeplitz operator.
- -
- To obtain some results on the spectrogram associated with the LCDWT.
The remaining part of this paper is organized as follows: In Section 2, we will state the necessary materials concerning the harmonic analysis related to the Dunkl transform and the linear canonical Dunkl transform studied in [9]. In Section 3, we will present and study a generalized windowed-type transform related to the LCDT. More precisely, we will prove a Plancherel-type, a Lieb-type theorem, and an inversion formula. Then, the study of localization operators theory in the context of the LCDWT is the focus of Section 4. Specifically, in the Schatten classes, we examine their boundedness and compactness, and in order to characterize functions with time–frequency content in a subset of finite measure, we lastly examine the spectral analysis related to the time–frequency Toeplitz operators. Additionally, we provide and study the spectrogram related to the LCDWT.
2. The Linear Canonical Dunkl Transform and Its Properties
We will outline the requirements for the Dunkl and linear canonical Dunkl transforms in this section. The primary sources include [1,2,3,4,31].
2.1. Dunkl Transform: Properties, Translation, and Convolution
Let be the orthonormal basis of , and for , we denote . The reflection in the hyperplane orthogonal to is defined by
Let be a finite subset of . If and if for all , , then is a root system.
It is quite obvious that the reflections associated with root system generate a finite group , called the reflection group. We note that there is a bijective correspondence between the conjugacy classes of reflections in W and the orbits in under the natural action of W.
Let . Fix a positive root system . We will assume that for all , we have .
If a positive function k defined in remains invariant under the action of the reflection group W, then it is considered as a multiplicity function.
In addition, the weight function is defined as
and we introduce the index
It is evident that is homogeneous of degree and W-invariant.
Finally, we introduce the well-known Mehta-type constant [32]:
For the finite reflection group W and for the multiplicity function k, the Dunkl operators are defined as follows. For any function ,
where , and is the space of functions of class on .
The Dunkl operators constitute a commutative system of differential-difference operators. Likewise, for each , we may define the Dunkl–Laplacian operator as
where Δ and ∇ denote the Euclidean Laplacian and the gradient operators on , respectively.
For , the system
admits a unique analytic solution on , called the Dunkl kernel. It has the following properties:
- has a unique holomorphic extension to .
- For all and , we have , and .
- For all and , we havewhere and . In particular, , for all .
- admits the following Laplace-type integral representation:where denotes the positive probability measure on , with support in of center 0 and radius (see [33]).
Let the space of Borel measurable functions f on satisfying
where Specifically, is a Hilbert space that has the scalar product
If denotes the space of all -valued functions on , then we set
as the subspace of radial functions in . Therefore, for any , there exists a unique function such that , for every .
Remark 1.
In [31], it is proved that for a radial function , the function G defined by is integrable with regard to the measure . More precisely,
where
The Dunkl transform of any function is defined by
The Dunkl transform satisfies the following properties (see [2,3]).
- For any , we have
- Inversion formula: If is a function such that its Dunkl transform is in , then
- The Dunkl transform is a topological isomorphism from the Schwartz space onto itself. If satisfiesthen,
- Plancherel-type relation: For all ,
- Parseval-type relation: For all ,
Proposition 1.
If (resp. , then
and
where is the function given by and is the space of -functions on which are of compact support.
Definition 1.
The Dunkl translation operator is defined on by (see [31])
The Wigner-type space of all functions that fulfill (22) point-wise is given by
The following properties of the Dunkl translation operator can be found in [31,34]:
- For all , we have
- For all ,
- For every ,
Remark 2.
Notice that Trimèche in [4] has defined the Dunkl translation on the space of -functions on as
where is the Dunkl transmutation operator.
Until now, an explicit formula for the Dunkl translation operator is known only in the following cases: If and , then for all and , we have
where
If , an explicit formula for the translation operator may also be obtained from the previous result. Additionally, we have the boundedness result shown below (see [34]).
Proposition 2.
Let . If , then for every ,
Moreover, if is radial, then
where is the function defined by and is the measure given by (11).
The subspace of radial functions in is denoted by . Following that, we have (see [34]):
Proposition 3.
- 1.
- If is positive, then for alland
- 2.
- For all , , we have
The Dunkl convolution product may be defined using the Dunkl translation operator (see [4,34]).
Definition 2.
The Dunkl convolution product of two functions is defined by
This transformation is commutative, associative, and meets the following properties (see [4,34]).
Proposition 4.
Let such that
- If and then belongs to and
- If , then for all and , the function and
- If , then if and only if , and in this case, we have
- If , thenwhere both sides are finite or infinite.
2.2. Linear Canonical Dunkl Transform
In this article, denotes a matrix in with For all and , we choose the principal branch of the power function as where .
In this subsection, we recall some results proved in [9,35].
Definition 3.
The linear canonical Dunkl transform of a function is defined by
where
Proposition 5.
We denote by the differential-difference operator given by
where .
Then we have the following relations:
- and are connected by
- We have for any ,
- The kernel satisfies
- For every we haveand
- For each ,
2.2.1. Particular Cases
- In the case , , coincides with the Fresnel transform associated with the Dunkl transform (see [9]):where
- In the case , is reduced to the Dunkl–Laplacian operator and coincides with the Dunkl transform (except for a constant unimodular factor ) (see [9]).
- When , corresponds to the following integral transform (see [9]):where
- In the case , coincides with the fractional Dunkl transform (see [9]):where and
2.2.2. Linear Canonical Dunkl Transform on ,
Definition 4.
For , the dilation operator and the chirp multiplication operator are defined by
Proposition 6.
The following equalities hold on .
- For all , we have
- For , we have
Theorem 1
(Riemann–Lebesgue’s lemma). For all , the linear canonical Dunkl transform belongs to and satisfies
The following lemma is easily shown using basic computation.
Lemma 1.
Given , then .
Theorem 2
(Plancherel’s Theorem).
- For every ,
- If , then and we have
- The linear canonical Dunkl transform has a unique extension to an isometric isomorphism of . The extension is also denoted by .
- For all , we have
- For all with
Definition 5.
Let and . Then the linear canonical Dunkl transform on is defined by
where is the Dunkl transformation on .
Theorem 3
(Young’s inequality). For and , the linear canonical Dunkl transform extends to a bounded linear operator on and we have
2.3. Generalized Convolution Product Associated with
In this subsection, we recall several results that were shown in [35].
Definition 6.
Let such that For suitable function f, we define the generalized translation operators associated with the operator by
where is the Dunkl translation operator.
We will rely on this definition for each function whose Dunkl translation is well defined. Thus from Definition 1, Proposition 2, Remark 2, and Proposition 3, we derive that the generalized translation (54) is well defined on the following spaces:
- .
- , .
- .
- , when .
Proposition 7.
Let such that Then the operators , satisfy
- and
- For all we have the product formula
- The operator is continuous from into itself, into itself, and on . More precisely, if we haveSimilarly, if we have
- When , for any , we have
- For all (resp. ) we havewhere is the inverse matrix of given in Lemma 1.
- For all we have
Corollary 1.
Let such that and Then for each we have
where is the inverse matrix of given in Lemma 1.
Let such that . The generalized convolution product associated with of two suitable functions f and g on , is the function defined by
for all x such that the integral exists. The elementary properties of convolutions are summarized in the following proposition.
Proposition 8.
Assuming that all integrals in question exist, then we have
- 1.
- 2.
Proposition 9
(Young’s Inequality). Let such that and let such that If , and then and we have
Moreover, if we assume that and then
Proposition 10.
Let such that
- If and , then for all ,where is the inverse matrix of given in Lemma 1.
- If and , then
- If , then we have
- If , the previous three results are valid without requiring the functions to be radials.
3. The Linear Canonical Dunkl Windowed Transform
In this section, we introduce the continuous linear canonical Dunkl windowed transform associated with the operator and we give some harmonic analysis properties for it. We will denote by the space of Borel measurable functions such that
where
Definition 7.
Let and The modulation of the function ϕ by t is defined as follows:
In the following proposition, we state some properties of the modulation .
Proposition 11.
Let . Then
- For all we have
- For all we have
Proof.
Relation (67) is as an immediate consequence of the relations (48), (54), (27) and (66). On the other hand, from the relations (48), (54), (27), (24), and (66), we have
□
Definition 8.
Let . We consider the family , , defined by
For any function , we define its linear canonical Dunkl windowed transform (LCDWT) by
Remark 3.
- We have for all ,
- The linear canonical Dunkl windowed transform can be written as
Using basic computation, we prove the following result.
Lemma 2.
If , then for every ,
Proposition 12.
Let . Then for every belongs to and we have
Proof.
Let . Using (70), Cauchy–Schwartz’s inequality and Relation (71), we have for every
Thus, belongs to and we have
□
Theorem 4
(Inversion-type Relation). Let ϕ be a function in such that . Then, for any function f in such that belongs to we have, for almost everywhere,
To prove this theorem, we need the following lemma.
Lemma 3
( inversion formula). Let ϕ be as above. Then, for any function f in we have
where is the ball of center 0 and radius and the limit is in
Proof.
As f is in , then from (72), we have
Thus, using Proposition 4, (34), (35), and (65), we obtain
Using the hypothesis on and by a standard analysis, we obtain
Thus, using this relation and the relation (59), we obtain
Moreover, since f is in we have
the limit is in Thus, by this relation and (79), we derive that for
The limit is in □
Proof of Theorem 4.
Using (81), we derive that for almost every
By standard analysis, we obtain for almost every
We consider for , the sequence given by
This sequence satisfies the following:
On the other hand, for all we have
By standard analysis, we prove that the function
is integrable on with respect to the measure Then, by applying the dominated convergence theorem to the relation (82), we obtain, for almost every ,
Thus, the theorem is proved. □
Proposition 13.
If , then for every ,
Corollary 2
(Plancherel’s formula). Let . If , then and we have
Proposition 14.
Let and . Then for all
Proof.
We have
Using Proposition 12 and Corollary 2, we obtain the desired result. □
Proposition 15.
Let ϕ be in . Then, is a reproducing kernel Hilbert space in with kernel function
The kernel is pointwise bounded:
Proof.
Let . We have
Using Parseval’s relation (83), we obtain
On the other hand, using Proposition 4, one can easily see that for every , the function
belongs to . Therefore, the result is obtained. □
4. Localization Operators for the Linear Canonical Dunkl Windowed Transform
Let be the singular values of the compact operator , which are eigenvalues of the non-negative self-adjoint operator .
All compact operators with singular values in are called the Schatten class , .
These spaces are equipped with the norm
Moreover, let , the set equipped with the norm,
Definition 9.
The trace of an operator A in is defined by
where is any orthonormal basis of .
Remark 4.
If T is positive, then
Moreover, a compact operator T on the Hilbert space is Hilbert–Schmidt if the positive operator is in the space of trace class . Then,
for every orthonormal basis of .
Definition 10.
Let ς be Borel measurable function on . For two Borel measurable radial functions on , we define the two-wavelet localization operator related to the linear canonical Dunkl windowed transform on , , by
In accordance with the different choices of the symbols and the different continuities required, we need to impose different conditions on u and v, and then we obtain an operator on .
It is often more convenient to interpret the definition of in a weak sense, that is, for f in , , and g in ,
By straightforward calculus, one has the following result:
Proposition 16.
The adjoint of the linear operator , , is . Moreover, we have
For simplicity’s sake, we will refer to as localization operators.
4.1. Boundedness of on
Our goal here is to show that
is a bounded operator for each symbol , . We first consider this problem for in and next in and we then conclude by using interpolation theory.
In this section, u and v will be two radial functions on such that
Proposition 17.
Let ; then the localization operator is in and we have
Proposition 18.
Let , then the localization operator is in and we have
Proof.
For all functions f and g in , we have, from Hölder’s inequality,
Using Plancherel’s formula for and , given by the relation (84), we obtain
Thus,
As desired. □
Consequently, we obtain the following result.
Theorem 5.
Let ς be in , . Then, there exists a unique bounded linear operator
such that
Proof.
Let f be in . We consider the following operator
given by
Thus, by the Propositions 17 and 18,
and
Hence, using (97), (98) and the interpolation theorem ([36], Theorem 2.11) may be uniquely extended to a linear operator on such that
As (99) is true for arbitrary functions f in , then we obtain the desired result. □
4.2. Schatten–von Neumann Properties for
The main result of this subsection is to prove that the localization operators are in .
Proposition 19.
If , then is in the Hilbert–Schmidt class , such that
Proof.
Proposition 20.
Let . Then the localization operator is compact.
Proof.
Let be a sequence in such that in as . Then, by Theorem 5,
Thus, in , as . Moreover, by Proposition 19, the operator is in ; hence, it is compact. Consequently, is compact. □
Theorem 6.
If ς is in , then is in and we have
where is given by
Proof.
As is in , then by Proposition 19, . Thus by the canonical form of compact operators (see [36], [Theorem 2.2]), there exists an orthonormal basis for the orthogonal complement of the kernel of the operator , consisting of eigenvectors of and , an orthonormal set in , such that
where are the positive singular values of corresponding to . Then, we obtain
Thus, by Fubini’s theorem, Cauchy–Schwarz’s inequality, Bessel inequality, and relations (71) and (70), we obtain
Thus,
On the other hand, it is clear that belongs to , and by (103), we obtain
Then from Fubini’s theorem, we obtain
Thus, using Plancherel’s formula for , , we obtain
□
Corollary 3.
The trace formula for ς in is as follows:
Proof.
Let be an orthonormal basis for . Then by Theorem 6, is in . Therefore, by Relation (90) and Parseval’s identity,
□
The main result of this subsection is shown in the following corollary.
Corollary 4.
If , , then is in and we have
Proof.
This is the consequence of interpolation theorems (see [36], [Theorems 2.10 and 2.11]), Proposition 18, and Theorem 6. □
Notice that, in the case that and is real valued and positive, we derive that is non-negative. Then, by Corollary 3 and Relation (91), we have
Corollary 5.
Let such that , and let be any real-valued and positive function. Then, , are positive trace class operators with
for every .
Proof.
Let and . Then, by [37], [Theorem 1], we have for every ,
□
5. Spectral Analysis for the Generalized Concentration Operator
Here, we will assume that the window function such that
We denote by
- the adjoint of given by
- the orthogonal projection from onto defined by
- the orthogonal projection from onto the subspace of functions whose supports are in the subset , that is,where denotes the characteristic function of the subset U of .
Our main focus in this section is on the concentration operator , which can be written as
where , and U is a subset of , such that .
5.1. The Range of the LCDWT
As , then is the integral operator such that
with integral kernel given by (86).
As is the integral kernel of an orthogonal projection, it satisfies
and
If is an orthonormal basis of , can be expanded as
Definition 11.
We define the spectrogram of f with respect to ϕ by
Definition 12.
We define the deformed Calderón–Toeplitz operator
by
Proposition 21.
The operator is trace class that fulfills
and
Proof.
For all
Then we have (113), and the operator is bounded and non-negative.
On the other hand, the operator can be written as
Therefore,
Consequently, the deformed Calderón–Toeplitz operator and the time–frequency operator are connected by
□
Based on the last proposition, we may conclude that and share the same spectral properties. Specifically, this leads to the following theorem.
Theorem 7.
The deformed Calderón–Toeplitz operator is compact and even trace class with
where
Proof.
Recall that the operator is bounded and positive. Now, let be an arbitrary orthonormal basis for Then, if we denote by then is an orthonormal basis for .
Thus, by (94) and Fubini’s theorem,
By Remark 4 and Definition 9, it follows that , with □
Let be the operator defined by Compared to , has the benefit of being defined on , which makes it easy to link its spectral properties to its integral kernel.
Given that is trace class and non-negative, the decomposition
implies that is also positive and trace class with
In addition, we have the following result.
Proposition 22.
The trace of is given by
Proof.
As is positive, then
On the other hand, using the fact that the space is a reproducing kernel Hilbert space with kernel , we obtain that for
That is, has integral kernel
Therefore,
where
By (107), we have
□
5.2. Spectral Properties
The spectral theorem provides the following spectral representation:
and this is because is compact and self-adjoint. Here, is the orthonormal set of eigenfunctions, corresponding to the positive eigenvalues , which are arranged in a non-increasing manner. That is, , and from Relation (96),
We can infer from (126) and (114) that the deformed Calderón–Toeplitz operator
is diagonalizable as
where .
Lemma 4.
We have, for all
Proof.
From Proposition 15, we have, for all the function is in Therefore, using the properties of the kernel of the reproducing kernel Hilbert space, we obtain
Let be an orthonormal basis of (eventually empty). Hence, is an orthonormal basis of ; therefore, the reproducing kernel can be written as
Then
and the conclusion follows. □
Let and define the quantity
The following estimate for the eigenvalue distribution is then obtained by the use of a simple adaption of the proof of Lemma 3.3 in [38].
Proposition 23.
If , then
5.3. Spectrogram of a Subspace
Let be the orthogonal projection onto V, with projection kernel , that is,
where V is an n-dimensional subspace V of
Remember that if is any orthonormal basis of V, then
Notice that the choice of orthonormal basis for V has no effect on the kernel .
Definition 13.
Let V be an n-dimensional subspace V of Then, the spectrogram of V with respect to ϕ is given by
Proposition 24.
The spectrogram satisfies
Proof.
As
then we have the desired result. □
Definition 14.
Let V be an n-dimensional subspace V of Then, the following defines the time–frequency concentration of V in U:
Then, from (134),
Theorem 8.
Using the first n eigenfunctions of , which correspond to the n biggest eigenvalues , the n-dimensional signal space maximizes the regional concentration , and we have
Proof.
We have
Moreover, the min–max lemma for self-adjoint operators states that (see, e.g., Sec. 95 in [39])
Thus, the number of orthogonal functions with a well-concentrated spectrogram in U is determined by the eigenvalues of . Consequently,
The time–frequency operator has optimum cumulative time–frequency concentration within U for its first n eigenfunctions, according to the min–max characterization of compact operator eigenvalues, that is,
Therefore, any n-dimensional subset V of cannot be better concentrated in U than i.e.,
□
Notice that we have
5.4. Accumulated Spectrogram
Let called the accumulated spectrogram, where we assume that is the smallest integer greater than or equal to , and
Then,
Note that
Moreover, as
then
fulfills
Moreover, we have the following lemma.
Lemma 5.
If , then
Proof.
Let and define . It follows that
As , we obtain
Therefore
As we obtain the desired result. □
Consequently, when the eigenvalues are close to 1, then Moreover, we have the following result bounding the error between and
Proposition 25.
We have
Proof.
Remark 5.
Let ϕ be in . We proceed as in [26], and we define the modulation of ϕ by t otherwise, as follows:
Subsequently, we define the generalized windowed transform as follows:
It is clear that
Thus, by involving Plancherel’s formula (18), we derive that the two integral transforms are equivalent and then all results proved for one are valuables for the second. Therefore, we claim that all results proved in this paper for the LCDGT are valuables for the integral transform and it is sufficient to replace ϕ by to derive the analog results.
6. Conclusions and Perspectives
In the present paper, we accomplished three major objectives. First, we investigated the linear canonical Dunkl windowed transform and studied its elementary properties. Then, we introduced the localization operators associated with the LCDWT and studied their trace class and Schatten–von Neumann class properties. As a side result, the spectrograms associated with the LCDWT were studied in detail. Finally, we indicate that in the future work, we will study other applications of the LCDWT, such as the uncertainty principles, which set limitations for a non-trivial signal and its LCDWT to be both well localized in the time–frequency plane.
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 & 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. KFU251253].
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 reviewers for their valuable comments, which helped improve this article. The second author acknowledges the assistance of Man Wah Wong and Khalifa Trimèche.
Conflicts of Interest
The authors declare that they have no conflict of interest.
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