Abstract
The linear canonical Fourier transform is one of the most celebrated time-frequency tools for analyzing non-transient signals. In this paper, we will introduce and study the deformed Gabor transform associated with the linear canonical Dunkl transform (LCDT). Then, we will formulate several weighted uncertainty principles for the resulting integral transform, called the linear canonical Dunkl-Gabor transform (LCDGT). More precisely, we will prove some variations in Heisenberg’s uncertainty inequality. Then, we will show an analog of Pitt’s inequality for the LCDGT and formulate a Beckner-type uncertainty inequality via two approaches. Finally, we will derive a Benedicks-type uncertainty principle for the LCDGT, which shows the impossibility of a non-trivial function and its LCDGT to both be supported on sets of finite measure. As a side result, we will prove local uncertainty principles for the LCDGT.
Keywords:
Dunkl transform; linear canonical transform; generalized translation; generalized convolution; uncertainty principles MSC:
47G10; 42B10; 47G30
1. Introduction
Dunkl operators were introduced and studied by Charles Dunkl [] as part of the extension of the classical theory of spherical harmonics. Besides their mathematical relevance, these operators are also applied in quantum mechanics in the study of one-dimensional harmonic oscillators governed by Wigner’s commutation rules. The Dunkl transform associated with a reflection group W and a non-negative multiplicity function k is defined for any function by
where denotes the Dunkl kernel and is a weighted measure. The Dunkl transform can be extended uniquely to an isometric isomorphism on and provides a natural extension of the usual Fourier transform for . For a detailed perspective regarding the Dunkl transform, we refer the reader to [,,] and the references therein.
In the context of time-frequency analysis, many important developments have already been reported in the framework of the Dunkl setting. For example, in [,,], the authors have derived several uncertainty inequalities for the Dunkl-Gabor transform. Additionally, several versions of quantitative uncertainty principles associated with the continuous Dunkl wavelet transform have been studied in [,].
In view of the contemporary developments of deformed Gabor transforms, we are deeply motivated to study a novel Gabor transform by using the combination of generalized translation and modulation related to the linear canonical Dunkl transform obtained in [].
In order to investigate the conservation of information and uncertainty under linear mappings of a phase space, Collins [] in paraxial optics and Moshinsky-Quesne [] in quantum mechanics have separately invented the classical linear canonical transform (LCT), which is an integral transform associated with a general homogeneous linear lossless mapping in phase space that depends on three parameters independent of the phase space coordinates. The three parameters constitute an augmented matrix consisting of a uni-modular matrix . It maps the position x and the wave number y into
where Any convex body can be transformed into another convex body while maintaining the body’s area. These changes make up the homogeneous special group .
The linear canonical transform of any function f with respect to a real augmented matrix
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 [,,,,,,,]. Moreover, the LCT might be a potential tool for solving complex interface problems [], and it could benefit from the adaptive high-order algorithms for solving large-scale problems [].
Recently the LCT has gained considerable attention and has been extended to a wide class of integral transforms; see, for example, refs. [,,,,,,,] and the references therein.
The LCT’s application areas have expanded exponentially over the past few decades, making it appropriate for examining deep issues in signal analysis, filter design, phase retrieval problems, pattern recognition, radar analysis, holographic three-dimensional television, quantum physics, and many other fields, such as uncertainty principles [], Poisson summation formulae [], convolution theorems [], and sampling theorems []. For further information about the LCT and their applications, see [,,,,,,].
This paper has two objectives. First by using the main tools related to the linear canonical Dunkl transform (LCDT) [], we present and study a new Gabor-type transform, which we will call: the linear canonical Dunkl-Gabor transform (LCDGT). Then, we will show the main theorems of harmonic analysis for this transformation.
Our second aim in this paper is to study and prove some uncertainty inequalities for our new Gabor transform, such as local-type, Benedicks-type, Pitt-type and Beckner-type uncertainty principles. Consequently, we will use a variety of methods, such as generalized entropy and the contraction semigroup method of the homogeneous integral transform to derive several types of Heisenberg’s uncertainty inequality.
Notice that uncertainty principles play an important role in both quantum mechanics and harmonic analysis, especially in time-frequency analysis. They have gained considerable attention and have been extended to a wide class of integral transforms; see, for example, reference [].
The main objectives of this paper are described as follows:
- -
- To introduce the notion of linear canonical Gabor transform in the Dunkl setting (LCDGT).
- -
- To derive some fundamental properties of the LCDGT, such as orthogonality relation, Young’s and inversion formula.
- -
- To investigate for the LCDGT several versions of the Heisenberg-type uncertainty principle via various techniques, including generalized entropy, the semigroup method of contraction and others.
- -
- To obtain certain concentration uncertainty principles for the LCDGT on sets of finite measure.
This paper’s remaining sections are 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 []. In Section 3, we will present and study a generalized Gabor-type transform related to the LCDT. More precisely, we will prove a Plancherel-type, a Lieb-type theorem, and an inversion formula. Section 4 is devoted to Heisenberg-type uncertainty principles associated with the LCDGT. The Pitt-type and logarithm uncertainty inequalities for the LCDGT are presented in Section 5. Finally, in Section 6, we will obtain some concentration uncertainty principles, including Benedick-type and local-type uncertainty principles.
2. Linear Canonical Dunkl Transform: Operators and Properties
In this section, we shall present the prerequisites concerning the linear canonical Dunkl transform. First, we will briefly review the conventional Dunkl operators, Dunkl convolutions and the corresponding Dunkl transform. The main references are [,,,,].
2.1. Dunkl Transform: Properties, Translation and Convolution
Let be the orthonormal basis of and for , we denote by . 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 fix a positive root system , for , and we will suppose that for all , we have .
If a function remains invariant under the action of the reflection group W, then it is considered as a multiplicity function.
Moreover, we define the weight function 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:
For any function , the Dunkl operators associated with the finite reflection group W and multiplicity function k are defined by
where , and is the space of functions of class on .
The Dunkl operators form a commutative system of differential-difference operators. Similarly, we can define the Dunkl-Laplacian operator , for any , as
where 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 []).
Let the space of measurable functions f on satisfying
where In particular, constitutes a Hilbert space equipped with 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 all .
Remark 1.
Using the homogeneity of , it is proved in [] that for a radial function , the function F defined by is integrable with respect to the measure . More precisely,
where
The Dunkl transform of any function is defined by
In the following, we state some properties of the Dunkl transform (see [,]).
- 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 we take such thatthen,
- Plancherel’s Formula: for any ,
- Parseval’s Formula: for any , we have
Proposition 1.
For all f in (resp. , we have the following relations:
and
where is the function defined by and is the space of -functions on , which are of compact support.
Definition 1.
The Dunkl translation operator is defined on by (see [])
Let be the Wigner-type space of all functions, which satisfies (22) point-wise. That is
The Dunkl translation operator satisfies the following properties (see [,]):
- For all , we have
- For any , we have
- For all f in , we have
Remark 2.
We note that, in [] Trimèche has defined the Dunkl translation on the space of functions of classes 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
From this, one can also give an explicit formula for the translation operator in the case of . Moreover, we have the following boundedness result (see []).
Proposition 2.
Let . Then, for any , we have
Moreover, if is radial, then
where is the function given by and is the measure given by (11).
Let be the subspace of radial functions in . Then, we have the following proposition (see []).
Proposition 3.
- 1.
- If is non-negative, then we haveand
- 2.
- For all , , we have
The Dunkl convolution product may be defined by using the Dunkl translation operator (see [,]).
Definition 2.
The Dunkl convolution product of any is defined by
The Dunkl convolution is both associative and commutative and satisfies the following properties (see [,]).
Proposition 4.
Let such that
- 1.
- If and then belongs to and
- 2.
- If , then for all and , the function and
- 3.
- If , then if and only if , and in this case, we have
- 4.
- If , thenwhere both sides are finite or infinite.
2.2. Linear Canonical Dunkl Transform
In this article, denotes a matrix in with In this subsection, we recall some results proved in [,].
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 .
- 1.
- The operator is related to the deformed Laplace operator by
- 2.
- For every
- 3.
- For each , the kernel of the linear canonical Dunkl transform satisfies the following:
- 4.
- For every , we haveand
- 5.
- For each ,
2.2.1. Particular Cases
- In the case , , coincides with the Fresnel transform associated with the Dunkl transform (see []):where
- In the case , is reduced to the deformed Laplace operator and coincides with the Dunkl transform (except for a constant unimodular factor ) (see []).
- When , coincides with the following integral transform (see []):where
- In the case , coincides with the fractional Dunkl transform (see []):where and
2.2.2. Linear Canonical Dunkl Transform on ,
Definition 4.
The chirp multiplication operator , and the dilation operator are defined, respectively, by
We recall in the following proposition some useful properties of and .
Proposition 6.
The following equalities hold on .
- 1.
- For all , we have
- 2.
- For , we have
Theorem 1
(Riemann–Lebesgue’s lemma). For all , the linear canonical Dunkl transform belongs to and satisfies
Theorem 2
(Plancherel’s Theorem).
- 1.
- For every ,
- 2.
- If , then , and we have
- 3.
- The linear canonical Dunkl transform has a unique extension to an isometric isomorphism of . The extension is also denoted by .
- 4.
- For all , we have
- 5.
- For all with
Definition 5.
For each , we define the linear canonical Dunkl transform on , by
where is the Dunkl transformation on .
Theorem 3
(Young’s inequality). Let , and let p be real number such that . Then, extends to a bounded linear operator on , and we have
2.3. Generalized Convolution Product Associated with
In this subsection, we recall some results proved in [].
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.
Proposition 7.
Let such that Then, the operators , satisfy:
- 1.
- and
- 2.
- For all we have the product formula
- 3.
- The operator is continuous from into itself, into itself and on . More precisely, if , we haveSimilarly, if , we have
- 4.
- When , for any , we have
- 5.
- For all (resp. ), we have
- 6.
- For all , we have
Corollary 1.
Let such that and Then, for each , we have:
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, 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
- 1.
- If and , then for all ,
- 2.
- If and , then
- 3.
- If , then we have
- 4.
- If , the previous three results are valid without requiring the functions to be radials.
3. The Linear Canonical Dunkl-Gabor Transform
In this section, we introduce the continuous linear canonical deformed Gabor transform associated with the operator , and we give some harmonic analysis properties for it. We will denote by the space of measurable functions such that
where
Definition 7.
Let and The modulation of the function φ by t is defined as:
In the following proposition, we state some properties of the modulation .
Proposition 11.
Let . Then,
- 1.
- For all , we have
- 2.
- For all , we have
Proof.
Definition 8.
Let . We consider the family , defined by:
For any function , we define its linear canonical Dunkl-Gabor transform (LCDGT) by
Remark 3.
- 1.
- We have for all :
- 2.
- The linear canonical Dunkl-Gabor transform can be written as:
- 3.
- A simple calculation implies that, for any and any ,where for all ,and
Using basic computation, we prove the following result.
Lemma 1.
If , then for every ,
Proposition 12.
Let . Then, for every belongs to and we have
Proof.
Theorem 4
inversion formula). Let with . Then, for any function , we have
in and satisfies
For proof this theorem, we need the following Lemmas.
Lemma 2.
Let φ be as above. Then, for any function , we have
where for any positive integer j
Proof.
We have
Then, using the hypothesis on and Fubini’s theorem, we derive that, for all ,
Moreover, involving the hypothesis on and by a standard analysis, we obtain
Thus, we derive
The proof is complete. □
Lemma 3.
Let φ be as above. For any positive integer j, we define the function
Then, we have
Proof.
Using the hypothesis on , (79) and the Cauchy–Schwarz inequality, we infer that . Now, we will to prove that . Firstly, we prove that . Indeed, from (66), we have
Thus, belongs to On the other hand, by Fubini’s theorem and the relation (27), we have
Therefore, belongs to Moreover, by Fubini’s theorem, we have
The proof is complete. □
Proof of Theorem 4.
It follows from Proposition 4, Lemma 2 and Lemma 3 that and
Then, by Plancherel’s Formula (48) and the fact that pointwise as , we have:
as , which achieves the proof. □
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. □
4. Heisenberg-Type Uncertainty Principles for the LCDGT
The window function will be non trivial and radial in
4.1. -Heisenberg-Type Uncertainty Inequalities
We begin this subsection by the following Heisenberg uncertainty principle for the LCDT.
Proposition 15.
For , there exists a positive constant , such that for every the following inequality holds
For , .
Proof.
Involving the Heisenberg uncertainty principle for the Dunkl transform (see []) and the identity (34), we derive the result. □
Theorem 5.
Let . Then, for every
Proof.
Proposition 16
(Nash-type Inequality for ). Let . Then, there is a constant such that, for every
4.2. Entropic Uncertainty Inequality
Consider to be a probability density function on that is,
According to Shannon [],
defines the k-entropy of the probability density function on .
Therefore, whenever the preceding integral on the right side is properly defined, we can extend the definition of the k-entropy of a nonnegative measurable function on . Then, we have the following result.
Proposition 17.
For every
Proof.
Assume that By (75), we have
Thus, Next, let
Therefore and Then, Moreover,
which implies
Using the fact that we deduce that
The proof is complete. □
Using the k-entropy of the linear canonical Dunkl-Gabor transform, we can obtain another version of the Heisenberg uncertainty principle for .
Theorem 6.
Let . Then, for every , we have
where
Proof.
Let , be the function defined on by
Using basic computation, we obtain
In particular, the measure is a probability measure on . As is a convex function over , we can use Jensen’s condition for convex functions to obtain
Therefore,
Assume that Then, by Proposition 17, we obtain
However, the expression
attains its upper bound at
and consequently
where
Hence,
Now by substituting f by , by , by and since the above inequality gives
By (73), we have
If we choose
then we obtain
where
Hence, the desired result follows from the previous inequality by substituting f by and by □
Remark 4.
If , then
4.3. -Heisenberg’s Uncertainty Principle
In this subsection, we will prove a general form of the Heisenberg-type uncertainty inequality for the linear canonical Dunkl-Gabor transform in the -setting. For , we set
By simple calculations, it is easy to check that for every there exists a positive constant C, such that
Lemma 4.
Let with . Then, there is a constant such that, for all and all ,
Proof.
Theorem 7.
Let and , with . Then, there is a constant such that for every
Proof.
Let such that and suppose that . If , then by the previous lemma, we have for all ,
On the other hand,
As is bounded for if , we obtain
from which, optimizing in , we obtain (91) for and .
Next, we assume that For and , we have , which is for becomes
It follows that
Minimizing over , we obtain
Together with (91) for , we obtain the result for . □
Corollary 3.
Let and . Then, there is a constant such that, for every ,
Proof.
The result follows from (81) and Theorem 7 for . □
5. Pitt and Logarithmic Inequalities for
Using (34) and the Pitt-type inequality in the Dunkl setting proved by Gorbachev et al. in [], we obtain that for each
where
and is the Euler’s Gamma function.
Our first aim here is to prove an analogue of Relation (92) for the LCDGT.
Theorem 8.
For any arbitrary the Pitt inequality for the linear canonical Dunkl-Gabor transform is given for by:
where is given by (93).
Proof.
Involving the Beckner-type inequality for the Dunkl transform (see []) and by (34), we derive that
for all . This inequality is related to the Heisenberg’s uncertainty principle and for that reason, it is often referred as the logarithmic uncertainty principle.
Our second main goal of this section is to establish an analogue of Inequality (98) for the LCDGT.
Theorem 9.
For all , we have
Proof.
Replacing f with in (98) gives
Integrating (99) with respect to the measure , we obtain
Using Plancherel’s Formula (81), we obtain
By (27) and Lemma 1, we have
Plugging the estimate (101) in (100) gives the desired inequality for the linear canonical Dunkl-Gabor transforms as
The proof is complete. □
A different proof of Theorem 9 is now presented by using Inequality (94).
Proof of Theorem 9.
Corollary 4.
Let . For any function f belongs to , the following inequality holds:
Proof.
Using Jensen’s inequality in (98), we obtain
which gives the desired result. □
Remark 6.
6. Uncertainty Inequalities for the LCDGT on Subsets of Finite Measures
The aim of this section is to prove some uncertainty inequalities for the LCDGT on subsets of finite measures, such as a Benedicks-type and a local-type uncertainty principles.
6.1. Benedicks Uncertainty Principle
Involving the Benedicks uncertainty principle for the Dunkl transform [] and the identity (34), if and are two subsets of with finite measure, then there exists a positive constant such that for any
Our primary interest in this section is to establish a Benedick–Amrein–Berthier’s uncertainty principle for the LCDGT.
Theorem 10.
For all , we have
Proof.
By Theorem 10, we can derive another type of Heisenberg’s uncertainty relation for the LCDGT.
Corollary 5.
For there is a constant , such that for every ,
6.2. Local-Type Uncertainty Principles
We begin this subsection by stating the following local uncertainty principle.
Proposition 18.
Let and let be a subset of finite measure . Then, there is a constant such that for every
Proof.
Involving the local uncertainty principle for the Dunkl transform [] and the identity (34), we derive the result. □
Consequently, we have the following local-type uncertainty principle for the LCDGT.
Theorem 11.
Let and let of finite measure . Then, for all ,
Proof.
For a subset E of , we set the following Paley–Wiener-type space :
By (18) and Theorem 11, we obtain the following result.
Corollary 6.
Let and let be a subset of finite measure . Then, for any ,
The following result can be obtained by interchanging the roles of f and in Proposition 18.
Corollary 7.
Let and be a subset of finite measure . Then, for all ,
By utilizing Corollary 7 and analogous concepts as presented in the proof of Theorem 11, we establish the following result.
Corollary 8.
Let and let be a subset of finite measure . Then, for all ,
Let F be a subset of . We define the generalized Paley–Wiener space as follows:
Applying Plancherel’s Formula (81), the definition of generalized Paley–Wiener space , and the previous corollary, we obtain the following:
Corollary 9.
Let E and F be two subsets of such that .
Let .
- 1.
- For any , we have
- 2.
- For any , we have
We end this section by the following Heisenberg-type uncertainty relation for the LCDGT.
Theorem 12.
For and , we have for every ,
where
Proof.
We end this paragraph by the following Faris–Price-type uncertainty inequality.
Theorem 13
(Faris–Price-type uncertainty principle for ). Let and . Then, there exists a constant such that for any measurable subset of finite measure , and all ,
Proof.
Suppose that . Therefore, for all ,
where denotes the ball of of radius r given by
By (75), we have for all ,
On the other hand, by simple calculation, we see that
Thus, we obtain
On the other hand, and again by Hölder’s inequality and Relation (75), we deduce that
Thus, for any ,
If we choose
we obtain
The proof is complete. □
Remark 7.
- 1.
- Let ϕ be in . We proceed as in [], to define the modulation of ϕ by t otherwise, as follow:Subsequently, we define the generalized Gabor transform as follow: For ,It is clear thatThus, by involving Plancherel’s Formula (18), we derive that the two integral transforms are equivalent and then all results proved for are valuables for . Therefore, we reclaim that all results proved in this paper for the LCDGT are valuables for the integral transform by replacing ϕ by to derive analogues results.
- 2.
- If , then all the results in this paper for the LCDGT are true without the assumption that the function φ is radial. It is enough to choose a function φ such that .
7. Conclusions and Perspectives
In the present paper, we accomplished two major objectives. We first studied the concept of the linear canonical Dunkl-Gabor transform and investigated its fundamental properties. Then, we examined other versions of the weighted uncertainty principles for this new transformation.
In future work, we will investigate further applications of the theory of time-frequency analysis for the linear canonical Dunkl transform and for other generalized linear canonical integral transforms.
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. KFU251102].
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 are deeply indebted to the referees for providing constructive comments to improve the contents of this article. The second author thanks Khalifa Trimèche and Man Wah Wong for their help.
Conflicts of Interest
The authors declare no conflicts of interest.
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