Abstract
The aim of this paper is to show some uncertainty inequalities for the linear canonical Dunkl transform (LCDT), including sharp Heisenberg-type, entropic-type, logarithmic-type, Donoho–Stark-type and local-type uncertainty principles.
MSC:
47G30; 42B10; 47G10
1. Introduction and Preliminaries
Dunkl operators play a vital role in the analysis of special functions exhibiting reflection symmetries, and recent years have witnessed significant advancements in harmonic analysis linked to root systems. These developments have attracted substantial interest within the field of mathematical physics. To proceed with precision, we first introduce some notation related to the Dunkl transform. For a more comprehensive overview of Dunkl theory, the reader is referred to [1,2,3].
Let denote the reflection across a hyperplane in orthogonal to a nonzero vector . Consider a root system such that , for all . The reflections corresponding to the elements of generate a finite subgroup W of the orthogonal group , known as the reflection group. Given a multiplicity function k defined on the root system , we define the associated weight function and the index by [1]
where denotes a system of positive roots associated with . In addition, we introduce the Mehta-type constant defined by
where denotes the standard Euclidean norm on .
The Dunkl operators are given by the following definition:
where is an orthonormal basis of .
The Dunkl kernel is the unique analytic function on that solves following the system:
This kernel satisfies the inequality
In addition, it admits a unique holomorphic extension to , and fulfills the identities
for all and .
For , and for a fixed multiplicity function k, the space consists of measurable functions f on satisfying the following:
where
Moreover, if , then [4]
where
For any function , the Dunkl transform is defined as
Let be a matrix in , with . The linear canonical Dunkl transform (LCDT) of a function is defined by
where
which satisfies the following: For every ,
This transformation generalizes the classical linear canonical transform (LCT), which was independently introduced by Collins [5] and Moshinsky–Quesne [6]. It serves as a powerful and versatile tool for exploring complex problems in areas such as signal processing, optics, and quantum physics [7,8,9,10,11,12]. In recent years, it has attracted considerable interest and has been studied to a broad class of integral transforms [13,14,15,16,17,18,19,20,21,22,23,24].
We define the differential-difference operator by the following:
where and is the Dunkl–Laplacian operator. Then, we have the following relations:
- and are connected by
- We have for any functions in the Schwartz space ,
- The kernel satisfies the following:
- For every we haveand
We define the chirp multiplication operator , by
and the dilation operator by
Then on ,
and
For all , its LCDT belongs to and satisfies the following relation:
- For all ,
- For , we have and
- The LCDT has a unique extension to an isometric isomorphism on , denoted also by .
- For all ,
- For every , such that its LCDT belongs to
where .
Since is well defined on , , then the LCDT is defined on by
Therefore, we have a Young-type inequality
for all and , where is the conjugate exponent of p, such that .
The existing literature presents numerous developments of uncertainty principles for integral operators generalizing the Fourier transform in one dimension, such as the fractional Fourier transform (FRFT) and the LCT [25,26,27,28,29,30,31,32,33], and in arbitrary dimensions, such as the free metaplectic transform (which can also be considered as a generalization of the LCT) [34,35,36,37,38,39,40]. Most of these uncertainty principles have been studied in one or two dimensions, and some of them have considered the general case of the N-dimension, among others only considering real signals or a particular case of the LCT.
In this paper, we will address the N-dimensional case, and prove some uncertainty inequalities for the LCDT, which is a generalization of the LCT, the Dunkl transform, the Fresnel–Dunkl transform, the fractional Dunkl transform, and other [13]. Note that the Fourier transform and the FRFT are two special cases of LCDT (for ). Our first result will be the following entropic uncertainty relation: For all ,
where
Then we will provide the following sharp Heisenberg-type uncertainty inequality, that is, for every ,
where the equality occurs in (63), if and only if, , . Here is the first term of the sequence of Laguerre functions, is the temporary dispersion, and is the frequency dispersion.
Equation (31) is an analogue of the well-known Heisenberg uncertainty inequality for the Dunkl transform [41,42,43,44,45]. For other uncertainty principles in the Dunkl setting, the reader is referred to [46,47,48,49,50,51,52].
More generally, for every , there is such that, for every ,
where the constant C does not depend on f, but in the general case it is not optimal. For the case , one can take . For , the -case of (32) is also studied in Theorem 3.
On the other hand, by showing Nash-type inequalities and Clarkson-type inequalities for the LCDT, we have obtained some uncertainty inequalities involving and -norms, . In particular (see also [50,53]), for every , we have
and
Other variations of such inequalities have been proven in this paper, for functions in , where .
In Section 2.5, we will show a Pitt-type inequality for the LCDT, that is, for all we have
where
Using this inequality, we derive the following logarithmic uncertainty inequality for the LCDT: For every , we have
In Section 2.6, we will prove some Donoho–Stark-type uncertainty principles for functions in , where . In particular, if a nonzero function is -concentrated on S for the -norm and its LCDT is -concentrated on for the -norm, then
where a nonzero function is said to be −concentrated on S for the -norm, if
where is the characteristic function. Notice that, if in (39), then g is concentrated on S, and is the support of g. Moreover, if is close to zero, then S is said to be the essential support of g. Therefore, (38) shows that the supports or the essential supports of a nonzero function f and its LCDT cannot be too small. Consequently, if a function satisfies
then f is the zero function [54].
In Section 2.7, we will show some local uncertainty inequalities, comparing the concentration of a function f or its LCDT, with the the time or frequency dispersions. Following this, we derive uncertainty inequalities involving the essential supports and time or frequency dispersions. Moreover, we obtain other general forms of Heisenberg’s uncertainty inequality.
2. Uncertainty Principles for the LCDT
Our first result will be an entropic-type uncertainty principle for the LCDT.
2.1. Entropic Uncertainty Inequality
A positive measurable function on that satisfies
is a probability density function on . Then
defines the entropy of on , in accordance with Shannon [55]. Therefore, if the integral in (42) is well defined, we extend (42) to any positive function defined on .
For any nonnegative measurable function g on , we define the weighted entropy of g by the following:
Then we have the following entropic uncertainty inequality.
Theorem 1.
For all , with , we have
Proof.
For , we have
Corollary 1.
For all ,
2.2. Sharp Heisenberg-Type Uncertainty Inequality
Let be such that , and let be the Laguerre polynomial given by the following:
where . Then we have the following proposition (see [56]).
Proposition 1.
For a fixed integer :
- 1.
- Letwhere . Then the sequence is an orthonormal basis for .
- 2.
- The sequence of generalized Laguerre functions is the basis of eigenfunctions of , for , such that
- 3.
- The sequence forms a complete set of eigenfunctions on for , such that
For , we define the sequences and of modified Laguerre functions as follows:
and
Proposition 2.
Let . Then,
- 1.
- The sequences and are two orthonormal bases of such thatand
- 2.
- Each of the sequences and forms a complete set on of eigenfunctions for the operatorssuch thatand
Proof.
The first result is an immediate consequence of the fact that any unitary operator transforms any orthonormal basis to an orthonormal basis. Moreover, as
then
and
For the second result, let . Then by a simple computation we have
Thus, using (12), (54), (59) we obtain (60), for , , and we obtain (61) for , , □
Theorem 2
(Sharp Heisenberg-Type Uncertainty Inequality).
Proof.
Corollary 2.
Let . Then for all ,
Proof.
Since the last corollary’s proof uses Hölder’s relation, then the result eliminates the special cases in which .
2.3. Heisenberg-Type Inequality Involving the -Norm
In this subsection, we will prove an -Heisenberg-type inequality for the LCDT. Our proof is motivated by [57], in which the -case is introduced and studied for Lie groups.
Lemma 1.
Let and . There is a constant , for which
Lemma 2.
Let , and . Then there exists such that for all ,
Proof.
By (23) we have,
It is straightforward to show that such that,
Consequently, we obtain
For , we get the result. □
Theorem 3.
Let , and let Then there is a positive constant C such that for every
Proof.
Assume first that Then by (73), we have
In addition,
If , then the function is bounded for all . Thus,
The result for and follows by minimizing the last inequality over .
On the other hand, let . For , we have , which for and , we obtain the result.
For , we obtain the inequality. □
Corollary 3.
For , there is such that, for every ,
2.4. Heisenberg-Type Inequalities Involving the -Norms
In this subsection, s and S are two positive, nonzero real numbers.
Theorem 4
(Nash-Type Inequality on ).
Let . Then for all
where
Proof.
The last theorem implies, in particular that,
Theorem 5
(Clarkson-Type Inequality on ).
Let . Then for all
where
Proof.
The following Heisenberg-type uncertainty inequalities are obtained from the relations (78) and (85).
Corollary 4.
Let . Then for every
where
In addition,
where
Remark 1.
For , we deduce the following Heisenberg-type uncertainty principles:
where
Theorem 6
(Nash-Type Relation on ).
Let . Then for every
where
Proof.
Theorem 7
(Clarkson-Type Inequality on ).
Let . Then for every
where
Proof.
Corollary 5.
Let . Then
Theorem 8
(Nash-Type Inequality on ).
Let . Then there exists a positive constant such that for all
Proof.
Corollary 6.
Let . Then for every
Theorem 9
(Clarkson-Type Inequality on ).
Let . Then there exists a positive constant such that, for every
Proof.
For and , we have
Firstly,
By Hölder’s inequality, we get
Combining the relations (114), (115), and (116), we get
By choosing , we conclude the result.
□
Theorem 10.
Let .
- 1.
- For all ,where
- 2.
- For all ,where
Corollary 7.
Let .
- 1.
- For all
- 2.
- For every
- 3.
- For everywhere
2.5. Pitt-Type Inequality
Pitt’s inequality explains the relation between the variance of a function and its Dunkl transform, which makes it fundamentally important in the Dunkl framework. An optimal version of this inequality has recently been proved in [52]. That is, for every ,
where
The following result is an analogue of (122) for the LCDT.
Theorem 11.
The Beckner-type inequality in the Dunkl setting states the following (see [52]): For all ,
The next theorem is an analogue of inequality (126) for the LCDT.
Theorem 12.
For every , we have
Proof.
Corollary 8.
For all , we have
Proof.
Applying Jensen’s inequality to (127), we obtain
which completes the proof. □
Remark 2.
2.6. Donoho–Stark-Type Uncertainty Inequalities
Let , and let . Then we say that a nonzero function , is −concentrated on S for the -norm, if
and we say that is -concentrated on for the -norm, if
Notice that if in (133) and (134), then is the support of f and is the support of .
In the remaining of this subsection will be in and will be two measurable subsets of with finite measure .
Theorem 13
(Donoho–Stark-Type Inequality on ).
Let . If a nonzero function is -concentrated on S for the -norm and its LCDT is -concentrated on Σ for the -norm, then
Proof.
Let . Then
Therefore from (23),
On the other hand, since f is -concentrated on S for the -norm,
Thus,
Corollary 9.
If a nonzero function is -concentrated on S for the -norm and its LCDT is -concentrated on Σ for the -norm, then
Theorem 14
(Donoho–Stark-Type Relation on ).
Let . If a nonzero function is -concentrated on S for the -norm and its LCDT is -concentrated on Σ for the -norm, then
Proof.
Let . Then we have
Thus by (28),
On the other hand, since f is -concentrated on S for the -norm,
Thus,
Corollary 10.
If a nonzero function , , is -concentrated on S for the -norm and its LCDT is -concentrated on Σ for the -norm, then
Let and let be the set of functions of whose LCDT are supported on . Then we say that f is -bandlimited on for the -norm if there is such that
Theorem 15.
If a nonzero function , , is -concentrated on S for the -norm and -bandlimited on Σ for the -norm, then
Proof.
Corollary 11.
If a nonzero function , , is -concentrated on S and -bandlimited on Σ for the -norm, then
Remark 3.
If we take in the previous inequalities, we derive the following Matolcsi–Szucs-type relations:
- 1.
- For every nonzero function , , we have
- 2.
- For all nonzero function , we have
- 3.
- For all nonzero function , , we have
- 4.
- For all nonzero function , , we have
2.7. Local Uncertainty Inequalities
In this section we will show another type of uncertainty relations involving general dispersions and the fraction of the norm on sets of finite measure. We start with the following lemma [59].
Lemma 3.
Let .
- 1.
- If , then
- 2.
- If , then
Theorem 16.
Let , and let be a subset of finite measure . Then for every , we have
where
Proof.
Let , and suppose that
Define and . Since
then by (28) we have
On the other hand, by (23) and Hölder’s inequality,
Therefore, from Lemma 3,
Choosing
we obtain the first inequality.
The second inequality holds if or Assume that
As above using (23), we obtain
Finally, by Lemma 3, we have
Therefore , and by replacing by , , in (156), we get
In particular, the inequality holds at the critical point
which implies that
where
which allows as to conclude.
Finally, since for , we have
then
Optimizing in , we obtain
Thus, using the first case (for ) we deduce that
The proof is complete. □
Remark 4.
Notice that, in the case of , one can provide an other inequality that improves that in Theorem 16. In fact, taking
and using the following relation,
we get for every ,
Corollary 12.
- 1.
- If , then there is , such that for any ε-bandlimited function f on S,
- 2.
- If , then there is , such that for any ε-bandlimited function f on S,
- 3.
- For all , there there is , such that for any ε-bandlimited function f on S,
Proof.
Corollary 13.
Let and . Then for all ,
where is the Lorentz space defined by the following norm:
Theorem 17.
Let and . Then for all ,
where
and
Proof.
Let , and . Then
From Theorem 16 and Lemma 3, we have
Moreover, it is easy to see that
Combining the relations (165), (166), and (167), we get
The first inequality follows by choosing
Now let and . Then from Theorem 16 and Lemma 3, we have
Combining the relations (165), (167), and (168), we get
Choosing
provides the second inequality.
Finally, given , and . Then from Theorem 16 and Lemma 3, we have
Combining the relations (165), (167), and (169), we get
The last inequality follows by choosing
□
Corollary 14.
Let . Then for every ,
where
Building on the ideas of Faris [60] and Price [61,62] in the classical setting, we will provide another local-type uncertainty inequality for the LCDT.
Theorem 18.
Let and . Then there is , such that for all and every subset of finite measure ,
Proof.
Corollary 15.
If , then for , , we have
where , are the constants given in Theorem 16.
Proof.
By (28) we have
Theorem 16 allows to conclude. □
Proposition 3.
If , then for , , we have
where , are the constants given by Theorem 16.
Proof.
Proposition 4.
Let , . If is -concentrated on Σ for the -norm, then
Proof.
Using the fact that f is -concentrated on , and Theorem 3, we obtain the result. □
In a similar way we have the following results.
Proposition 5.
If , then for all , , such that is -concentrated on Σ for the , we have
3. Conclusions and Perspectives
In the present paper, we proved some quantitative uncertainty principles for functions in the -spaces, comparing time or frequency dispersions, the supports or the essential supports of these functions. All these uncertainty inequalities show the impossibility of a function and its LCDT to be both well concentrated in the time-frequency plane.
In future work, we will study some qualitative uncertainty principles for the LCDT, including in particular Hardy and Beurling uncertainty principles. Moreover, we will extend some results proved in the recent paper [63] to arbitrary dimension.
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. KFU252995].
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Dunkl, C.F. Differential-difference operators associated to reflection groups. Trans. Am. Math. Soc. 1989, 311, 7–183. [Google Scholar] [CrossRef]
- Dunkl, C.F. Hankel transforms associated to finite reflection groups. Contemp. Math. 1992, 138, 123–138. [Google Scholar] [CrossRef] [Scilit]
- Trimèche, K. Paley-Wiener theorems for Dunkl transform and Dunkl translation operators. Integral Transforms Spec. Funct. 2002, 13, 17–38. [Google Scholar] [CrossRef] [Scilit]
- Rösler, M.; Voit, M. Markov processes related with Dunkl operators. Adv. Appl. Math. 1998, 21, 575–643. [Google Scholar] [CrossRef] [Scilit]
- Collins, S.A.J. Lens-system diffraction integral written in terms of matrix optics. J. Opt. Soc. Am. A 1970, 60, 1168–1177. [Google Scholar] [CrossRef] [Scilit]
- Moshinsky, M.; Quesne, C. Linear canonical transformations and their unitary representations. J. Math. Phys. 1971, 12, 1772–1780. [Google Scholar] [CrossRef] [Scilit]
- Barshan, B.; Kutay, M.A.; Ozaktas, H.M. Optimal filtering with linear canonical transformations. Opt. Commun. 1997, 135, 32–36. [Google Scholar] [CrossRef] [Scilit]
- Ozaktas, H.M.; Zalevsky, Z.; Kutay, M.A. The Fractional Fourier Transform with Applications in Optics and Signal Processing; Wiley: New York, NY, USA, 2000. [Google Scholar]
- Hennelly, B.M.; Sheridan, J.T. Fast numerical algorithm for the linear canonical transform. J. Opt. Soc. Am. A 2005, 22, 928–937. [Google Scholar] [CrossRef] [Scilit]
- Bultheel, A.; Marlínez-Sulbaran, H. Recent development in the theory of the fractional Fourier and linear canonical transforms. Bull. Belg. Math. Soc. Simon Stevin. 2007, 13, 971–1005. [Google Scholar] [CrossRef] [Scilit]
- Stern, A. Uncertainty principles in linear canonical transform domains and some of their implications in optics. J. Opti. Soc. Am. A 2008, 25, 647–652. [Google Scholar] [CrossRef] [Scilit]
- Xu, T.Z.; Li, B.Z. Linear Canonical Transform and Its Applications; Science Press: Beijing, China, 2013. [Google Scholar]
- Ghazouani, S.; Soltani, E.A.; Fitouhi, A. A unified class of integral transforms related to the Dunkl transform. J. Math. Anal. Appl. 2017, 449, 1797–1849. [Google Scholar] [CrossRef] [Scilit]
- Shah, F.A.; Tantary, A.Y. Linear canonical Stockwell transform. J. Math. Anal. Appl. 2020, 484, 123673. [Google Scholar] [CrossRef] [Scilit]
- Shah, F.A.; Tantary, A.Y. Linear canonical ripplet transform: Theory and localization operators. J. Pseudo-Differ. Oper. Appl. 2022, 13, 1–24. [Google Scholar] [CrossRef] [Scilit]
- Bahri, M.; Karim, S.A.A. Novel uncertainty principles concerning linear canonical wavelet transform. Mathematics 2022, 10, 3502. [Google Scholar] [CrossRef] [Scilit]
- Srivastava, H.M.; Tantary, A.Y.; Shah, F.A.; Zayed, A.I. An interplay of Ridgelet and linear canonical transforms. Mathematics 2022, 10, 1986. [Google Scholar] [CrossRef] [Scilit]
- Urynbassarova, D.; Teali, A.A. Convolution, correlation, and uncertainty principles for the quaternion offset linear canonical transform. Mathematics 2023, 11, 2201. [Google Scholar] [CrossRef] [Scilit]
- Kaur, N.; Gupta, B.; Verma, A.K.; Agarwal, R.P. Offset linear canonical Stockwell transform for Boehmians. Mathematics 2024, 12, 2379. [Google Scholar] [CrossRef] [Scilit]
- Yang, H.; Feng, Q.; Wang, X.; Urynbassarova, D.; Teali, A.A. Reduced biquaternion windowed linear canonical transform: Properties and applications. Mathematics 2024, 12, 743. [Google Scholar] [CrossRef] [Scilit]
- Dhaouadi, L.; Sahbani, J.; Fitouhi, A. Harmonic analysis associated to the canonical Fourier Bessel transform. Integral Transforms Spec. Funct. 2021, 32, 290–315. [Google Scholar] [CrossRef] [Scilit]
- Mejjaoli, H.; Negzaoui, S. Linear canonical deformed Hankel transform and the associated uncertainty principles. J. Pseudo-Differ. Oper. Appl. 2023, 14, 29. [Google Scholar] [CrossRef] [Scilit]
- Shi, J.; Liu, X.; Zhang, N. Generalized convolution and product theorems associated with linear canonical transform. SIViP 2014, 8, 967–974. [Google Scholar] [CrossRef] [Scilit]
- Wolf, K.B. Canonical transforms. II, complex radial transforms. J. Math. Phys. 1974, 15, 1295–1301. [Google Scholar] [CrossRef] [Scilit]
- Gao, W.B. New uncertainty principles in the linear canonical transform domains based on hypercomplex functions. Axioms 2025, 14, 415. [Google Scholar] [CrossRef] [Scilit]
- Guanlei, X.; Xiaotong, W.; Xiaogang, X. Uncertainty inequalities for linear canonical transform. IET Signal Process. 2009, 3, 392–402. [Google Scholar] [CrossRef] [Scilit]
- Bahri, M.; Abdul Karim, S.A. Fractional Fourier transform: Main properties and inequalities. Mathematics 2023, 11, 1234. [Google Scholar] [CrossRef] [Scilit]
- Mai, W.; Dang, P.; Pan, W.; Chen, X. Uncertainty principles for linear canonical transform. J. Math. Anal. Appl. 2025, 548, 129414. [Google Scholar] [CrossRef] [Scilit]
- Dang, P.; Deng, G.T.; Qian, T. A tighter uncertainty principle for linear canonical transform in terms of phase derivative. IEEE Trans. Signal Process. 2013, 61, 5153–5164. [Google Scholar] [CrossRef] [Scilit]
- Sharma, K.; Joshi, S. Uncertainty principle for real signals in the linear canonical transform domains. IEEE Trans. Signal Process. 2008, 56, 2677–2683. [Google Scholar] [CrossRef] [Scilit]
- Xu, G.L.; Wang, X.T.; Xu, X.G. On uncertainty principle for the linear canonical transform of complex signals. IEEE Trans. Signal Process. 2010, 58, 4916–4918. [Google Scholar] [CrossRef] [Scilit]
- Zhao, J.; Tao, R.; Li, Y.L.; Wang, Y. Uncertainty principles for linear canonical transform. IEEE Trans. Signal Process. 2009, 5, 2856–2858. [Google Scholar] [CrossRef] [Scilit]
- Zhao, J.; Tao, R.; Wang, Y. On signal moments and uncertainty relations associated with linear canonical transform. Signal Process. 2010, 90, 2686–2689. [Google Scholar] [CrossRef] [Scilit]
- Chen, X.; Dang, P.; Mai, W.X. Lp-theory of linear canonical transforms and related uncertainty principles. Math. Methods Appl. Sci. 2024, 47, 6272–6300. [Google Scholar] [CrossRef] [Scilit]
- Dias, N.C.; de Gosson, M.; Prata, J.N. A metaplectic perspective of uncertainty principles in the linear canonical transform domain. J. Funct. Anal. 2024, 287, 110494. [Google Scholar] [CrossRef] [Scilit]
- Zhang, Z.C. Uncertainty principle for real functions in free metaplectic transformation domains. J. Fourier Anal. Appl. 2019, 25, 2899–2922. [Google Scholar] [CrossRef] [Scilit]
- Zhang, Z.C. Uncertainty principle of complex-valued functions in specific free metaplectic transformation domains. J. Fourier Anal. Appl. 2021, 27, 68. [Google Scholar] [CrossRef] [Scilit]
- Zhang, Z.C. Uncertainty principle for free metaplectic transformation. J. Fourier Anal. Appl. 2023, 29, 71. [Google Scholar] [CrossRef] [Scilit]
- Jing, R.; Liu, B.; Li, R.; Liu, R. The N-dimensional uncertainty principle for the free metaplectic transformation. Mathematics 2020, 8, 1685. [Google Scholar] [CrossRef] [Scilit]
- Liang, P.; Dang, P.; Mai, W. Uncertainty principles for free metaplectic transformation and associated metaplectic operators. arXiv 2025, arXiv:2506.03724. [Google Scholar] [CrossRef] [Scilit]
- Rösler, M. An uncertainty principle for the Dunkl transform. Bull. Aust. Math. Soc. 1999, 59, 353–360. [Google Scholar] [CrossRef] [Scilit]
- Shimeno, N. A note on the uncertainty principle for the Dunkl transform. J. Math. Sci. Univ. Tokyo 2001, 8, 33–42. [Google Scholar]
- Ghobber, S. Shapiro’s uncertainty principle in the Dunkl setting. Bull. Aust. Math. Soc. 2015, 92, 98–110. [Google Scholar] [CrossRef] [Scilit]
- Soltani, F. Heisenberg-Pauli-Weyl uncertainty inequality for the Dunkl transform on Rd. Bull. Aust. Math. Soc. 2013, 87, 316–325. [Google Scholar] [CrossRef] [Scilit]
- Soltani, F. A general form of Heisenberg-Pauli-Weyl uncertainty inequality for the Dunkl transform. Integral Transforms Spec. Funct. 2013, 24, 401–409. [Google Scholar] [CrossRef] [Scilit]
- Soltani, F. Pitt’s inequalities for the Dunkl transform on Rd. Integral Transforms Spec. Funct. 2014, 25, 686–696. [Google Scholar] [CrossRef] [Scilit]
- Fei, M.; Wang, Q.; Yang, L. Sharper uncertainty principle and Paley–Wiener theorem for the Dunkl transform. Math. Meth. Appl. Sci. 2022, 45, 882–898. [Google Scholar] [CrossRef] [Scilit]
- Kawazoe, T.; Mejjaoli, H. Uncertainty principles for the Dunkl transform. Hiroshima Math. J. 2010, 40, 241–268. [Google Scholar] [CrossRef] [Scilit]
- Gallardo, L.; Trimèche, K. An Lp version of Hardy’s theorem for the Dunkl transform. Bull. Aust. Math. Soc. 2004, 77, 371–385. [Google Scholar] [CrossRef] [Scilit]
- Ghobber, S. Uncertainty principles involving L1-norms for the Dunkl transform. Integral Transforms Spec. Funct. 2013, 24, 491–501. [Google Scholar] [CrossRef] [Scilit]
- Ghobber, S.; Jaming, P. Uncertainty principles for integral operators. Studia Math. 2014, 220, 197–220. [Google Scholar] [CrossRef] [Scilit]
- Gorbachev, D.; Ivanov, V.; Tikhonov, S. Sharp Pitt inequality and logarithmic uncertainty principle for Dunkl transform in L2. J. Approx. Theory 2016, 202, 109–118. [Google Scholar] [CrossRef] [Scilit]
- Ghobber, S. A variant of the Hankel multiplier. Banach J. Math. Anal. 2018, 12, 144–166. [Google Scholar] [CrossRef] [Scilit]
- Donoho, D.L.; Stark, P.B. Uncertainty principles and signal recovery. SIAM J. Appl. Math. 1989, 49, 906–931. [Google Scholar] [CrossRef] [Scilit]
- Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef] [Scilit]
- Ben Saïd, S.; Kobayashi, T.; Ørsted, B. Laguerre semigroup and Dunkl operators. Compos. Math. 2012, 148, 1265–1336. [Google Scholar] [CrossRef] [Scilit]
- Ciatti, P.; Ricci, F.; Sundari, M. Heisenberg-Pauli-Weyl uncertainty inequalities and polynomial volume growth. Adv. Math. 2007, 215, 616–625. [Google Scholar] [CrossRef] [Scilit]
- Whittaker, E.T.; Watson, G.N. A Course of Modern Analysis; Cambridge University Press: London, UK, 1927. [Google Scholar]
- Mejjaoli, H.; Sraeib, N.; Trimèche, K. Lp local uncertainty principles for the Dunkl Gabor transform on Rd. Bol. Soc. Mat. Mex. 2020, 26, 1163–1182. [Google Scholar] [CrossRef] [Scilit]
- Faris, W.G. Inequalities and uncertainty inequalities. J. Math. Phys. 1978, 19, 461–466. [Google Scholar] [CrossRef] [Scilit]
- Price, J.F. Inequalities and local uncertainty principles. J. Math. Phys. 1983, 24, 1711–1714. [Google Scholar] [CrossRef] [Scilit]
- Price, J.F. Sharp local uncertainty principles. Studia Math. 1987, 85, 37–45. [Google Scholar] [CrossRef] [Scilit]
- Umamaheswari, S.; Verma, S.K. Uncertainty principles associated with the linear canonical Dunkl transform. arXiv 2025, arXiv:2507.00530. [Google Scholar] [CrossRef] [Scilit]
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