Abstract
The free metaplectic transformation is an N-dimensional linear canonical transformation. This transformation operator is useful, especially for signal processing applications. In this paper, in order to characterize simultaneously local analysis of a function (or signal) and its free metaplectic transformation, we extend some different uncertainty principles (UP) from quantum mechanics including Classical Heisenberg’s uncertainty principle, Nazarov’s UP, Donoho and Stark’s UP, Hardy’s UP, Beurling’s UP, Logarithmic UP, and Entropic UP, which have already been well studied in the Fourier transform domain.
Keywords:
free metaplectic transformation; uncertainty principle; Nazarov’s UP; Logarithmic’s UP; Beurling’s UP 100 MR Subject Classiflcation:
47B38; 42A38; 42B10; 70H15
1. Introduction
The free metaplectic transformation (FMT), which was first studied in Reference [1], is widely used in many fields such as filter design, pattern recognition, optics and analyzing the propagation of electromagnetic waves [2,3,4,5]. This useful transformation is also known as the N-dimensional nonseparable linear canonical transformation. In Table 1,
Table 1.
Transition from the free metaplectic transformation (FMT) to various N-dimensional transformation.
Some specific transformations can be obtained by taking special values for symplectic matrices.
The uncertainty principle (UP) is usually understood as the relationship between the simultaneous expansion of functions and its Fourier transform. In essence, uncertainty deals with the problem of concentration. Therefore, it can process the interaction of data loss, sparsity and bandwidth limitation in signal recovery. Plenty of scholars have studied and popularized UP [1,6,7,8,9].
In Reference [10], Qingyue Zhang studies the Zak transform and uncertainty principles associated with the linear canonical transform. In Reference [11], Haiye Huo studies the Uncertainty Principles (UP) for the offset linear canonical transformation (OLCT) and extends the existing uncertainty theory from the Fourier transform domain to the OLCT domain. Until now, most of the existing results are considered in one-dimension. For N-dimensional FMT, Zhichao Zhang has done a number of studies, for example, Reference [12]. He extends the classical N-dimensional Heisenberg’s uncertainty principle to the FMT. A lower bound for the complex signal’s uncertainty product is given in Reference [12], where are parameter matrices of the FMT. In Reference [13], he extends the classical N-dimensional Heisenberg’s uncertainty principle to the fractional Fourier transform (FRFT). To the best of our knowledge, there are no other results published about UPs associated with the FMT. Hence, in this paper, we mainly extend the various forms of N-dimensional UPs from the Fourier transform to the FMT domain.
The function denotes the N-dimensional Fourier transform (FT) of ,
and the inversion formula for the FT is shown to be
where and . On the basis of FT, the classical N-dimensional Heisenberg’s uncertainty principle is given by the following:
If and are arbitrary, then
where the norm of function f is -norm, that is,
Our paper is organized as follows—in the next section, we give some basic definitions and lemmas. In Section 3, we extend the corresponding results of the Classical Heisenberg’s uncertainty principle, Nazarov’s UP, Donoho and Stark’s UP, Hardy’s UP, Beurling’s UP, Logarithmic UP and Entropic UP to the free metaplectic transformation domain, respectively.
2. Definitions and Preliminaries
This section give some useful definitions and lemmas about the free metaplectic transformation.
Definition 1.
(Zhang [12]) The metaplectic operator of a function with the free symplectic matrix (equivalently ) is defined as
where , and are all real matrices satisfying the following constraints:
and where denotes an identity matrix. The corresponding inverse formula is given by , where
Here
For the convenience of writing, represents the inversion formula of the free metaplectic transformation about function f. The following definition is also a significant basic content for the study of free metaplectic transformation.
Definition 2.
(Zhang [12]) Let be the N-dimensional FT of and be the free metaplectic transformation of with the symplectic matrix Assume that Then we can define
(i) The covariance in the time domain:
where the moment vector in the time domain is
(ii) The covariance in the frequency domain:
where the moment vector in the frequency domain is
(iii) The covariance in the free metaplectic transformation domain:
where the moment vector in the free metaplectic transformation domain is
We first give the definition of infinitesimal of the same order.
Definition 3.
(Gordon, Kusraev and Kutateladze [14]) Let and be two infinitely small functions as We call an infinitesimal of the same order of , record as if
The following is the definition of the Schwartz class. It is useful in Section 3.
Definition 4.
(Grochenig [9]) The Schwartz class consists of all -functions f on such that
for all where for the partial derivative and for the multiplication operator. denotes the Schwartz class.
In addition, Gamma function will also be used in this paper.
Definition 5.
In real number field, Gamma function’s formula is that
As an extension of factorial, Gamma function is defined in the complex range of meromorphic function, which is usually written as . When the variable of a function is a positive integer, then
For a set the characteristic function of T is
Let be a bounded linear operator. Then the operator norm of P is
more theories on operator norm can be seen in Reference [15].
The key players are the transition from free metaplectic transform to Fourier transform and the research on norm of free metaplectic transformation. The corresponding results are given by the following lemmas.
Lemma 1.
Let and then the relationship between and is
Proof.
From Definition 1, for all the free metaplectic transformation of
Let then
Furthermore,
Taking modulus on both sides of the equation, then
that is,
□
Next, we provide the fundamental result for the free metaplectic transformation. The Parseval formula is generalized.
Lemma 2.
If , then
Proof.
3. Uncertainty Principles for the Free Metaplectic Transformation Domain
Apparently, there are many different forms of UP’s in the Fourier transform domain. Since the free metaplectic transformation is a generalized version of the Fourier transform and the linear canonical transform, it is natural and interesting to study this different forms of UP’s in the free metaplectic transformation domain. it can not give the exact location in both f and its free metaplectic transformation.
3.1. Classical Heisenberg’s Uncertainty Principle
In Reference [12], Zhang gives the measurement estimate about the lower bound of the product of two free metaplectic transformations under the premise of classical uncertainty. In this subsection, the lower bound of uncertainty corresponding to the free metaplectic transformation is given by the following theorem.
Theorem 1.
Let If and are the variances and the free metaplectic transformation of , then
where denotes the minimum singular value of matrix B.
Proof.
Applying N-dimensional Heisenberg’s uncertainty principle to , we have
Equality holds if and only if is an N-dimensional Gaussian function. The combination of (4), (5) and (7) yields
Let , then . Therefore,
Furthermore,
Since is real and symmetric matrix by definition, there exits unitary matrix P such that
where are eigenvalues of Now suppose that represents the minimum eigenvalue of matrix represents the maximum eigenvalue of matrix We have
Then the result can be written as follows,
Since the relationship the right of the inequality can be simplified as The equality holds if and only if
It is easy to see that if is finite, then it is minimized at that is ; similarly, is minimized at that is Therefore,
From the above discussion, if is an N-dimensional Gaussian function and
then the equality holds. □
In particular, the result of Theorem 1 is consistent with Reference [16] for the N-dimentional FRFT if
3.2. Nazarov’s UP
Measured by smallness of support, Nazarov’s UP was first proposed by F.L. Nazarov in 1993 [17]. It argues what happens if a nonzero function and its Fourier transform are only small outside a compact set. We first introduce Nazarov’s UP for the Fourier transform.
Proposition 1.
(Jaming [18]) There exists a constant such that for finite Lebesgue measurable sets and for every
where is the mean width of E. In geometry, the mean width is a measure of the "size" of a body.
Motivated by Proposition 1, we extend the Nazarov’s UP to the free metaplectic transformation domain.
Theorem 2.
There exists a constant such that for finite Lebesgue measurable sets and for every
where is the mean width of E.
3.3. Donoho and Stark’s UP
The classical uncertainty principle is based on the interpretation of the standard deviation as the size of the “essential support” of f. By considering other notions of the support, it lead to different versions of the uncertainty principle. Considering the concentration degree of energy in a certain area, the beautiful uncertainty principle in this case has been received by Donoho and Stark in Reference [7].
Definition 6.
(Donoho and Stark [7]) A function is ϵ-concentrated on a measurable set , if
Here is the complement of set T on The following proposition is the Donoho and Stark’s UP in fourier domain.
Proposition 2.
(Grochenig [9]) Suppose that and is -concentrated on and is -concentrated on . Then
Similarly, it can be extended to the free metaplectic transformation domain.
Theorem 3.
Suppose that and is -concentrated on and is -concentrated on . Then
Proof.
Without loss of generality, we assume that T and have finite measure. In this proof we introduce two operators that occur frequently in problems of band-limited functions. Let
and
With this notation f is -concentrated on T if and only if
and is -concentrated on if and only if
Since , we obtain that
Consequently
Next we compute the integral kernel and the Hilbert-Schmidt norm of .
Let then
It follows from that the inverse formula of the free metaplectic transformation. Clearly,
Since both T and have finite measure, the double integral converges absolutely. By Fubini’s theorem, the order of integration can be exchanged, that is,
where the kernel
The Hilbert-Schmidt norm (Appendix A) of is given by
So we have
therefore
Finally, by (10), (11) and the operator norm is dominated by the Hilbert Schmidt norm, we have
Consequently,
□
3.4. Hardy’s UP
Hardy’s UP was first introduced by G.H. Hardy in 1933 [19]. Its localization is measured by fast decrease of a function and its Fourier transform. Hardy’s UP in the Fourier transform domain [20] was given as follows.
Proposition 3.
(Escauriaza, Kenig, Ponce and Vega [20]) If and then Also, if then
where C is a constant,
Based on Proposition 2, we derive the corresponding Hardy’s UP for the free metaplectic transformation.
Theorem 4.
If and then Also, if then
where C is a constant,
Proof.
Clearly, the Proposition 2 can be received if and in Theorem 4.
3.5. Beurling’s UP
Beurling’s UP is a variant of Hardy’s UP. It implies the weak form of Hardy’s UP immediately. Beurling’s UP in the Fourier transform domain is as follows.
Proposition 4.
(Bonami, Demange and Jaming [21]) Let and Then
if and only if f may be written as
where A is a real positive definite symmetric matrix and P is a polynomial of degree
Here a polynomial of degree means that the degree of the highest degree term of a polynomial.
In particular, for the function f is identically 0. Beurling-Hrmander’s original theorem is the above theorem for and An extension to but still first has been given by S.C. Bagchi and S.K. Ray in Reference [22] in a weaker form. Then S.K. Ray and E. Naranayan give in the present form. Next, we formulate the Beurling’s UP for the free metaplectic transformation domain.
Theorem 5.
Let and Then
if and only if f may be written as
where A is a real positive definite symmetric matrix and P is a polynomial of degree
From Theorem 5, we know that it is not possible for a nonzero function f and its free metaplectic transformation to decrease very rapidly simultaneously.
3.6. Logarithmic’s UP
In Reference [23], a simple argument based on a sharp form of Pitt’s inequality is used to obtain a logarithmic estimate of uncertainty.
Proposition 5.
(Beckner [23]) For and
where
Here denotes the Schwartz class.
Therefore, the Pitt’s inequality can be extended to the free metaplectic transformation domain. In the following, we will denote by .
Theorem 6.
Proof.
Based on the generalized Pitt’s inequality for the free metaplectic transformation proposed in Theorem 6, we investigate the logarithmic UP associated with the free metaplectic transformation.
Theorem 7.
Let and then
Proof.
Let where is given by (12). Taking the derivative of about the variable , we obtain
where
By Theorem 6, we know for From assumptions and Lemma 2,
We get
Consequently,
□
3.7. Entropic UP
Entropic UP is a fundamental tool in information theory, quantum physical and harmonic analysis. Its localization is measured by Shannon entropy. Let be a probability density function on . The Shannon entropy of is denoted by
We revisit entropic UP associated with the Fourier transform in following proposition.
Proposition 6.
(Folland, Sitaram [24]) If and we have
whenever the left side is well defined.
Next, we propose the entropic UP in the free metaplectic transformation domain.
Theorem 8.
If and we have
4. Concluding Remarks
Our paper mainly studies the UP for the free metaplectic transformation of We first give the relationship between the Fourier transformation and the free metaplectic transformation by Lemmas 1 and 2, then we study the UP for the free metaplectic transformation with seven different forms. In Section 3, we first show that it can not give the exact location in both f and its free metaplectic transformation by Theorem 1. Then we give the Nazarov’s UP, Donoho and Stark’s UP, Hardy’s UP, Beurling’s UP, Logarithmic UP, and Entropic UP in the free metaplectic transformation domain by Theorems 2–8.
Author Contributions
Conceptualization, B.L. and R.L. (Rui Liu); data curation, B.L. and R.L. (Rui Li); formal analysis, B.L and R.L. (Rui Li) and R.L. (Rui Liu); funding acquisition, B.L. and R.L. (Rui Liu); supervision, B.L. and R.L. (Rui Li); writing—original draft, R.J.; writing—review editing, B.L and R.L. (Rui Liu). All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by National Natural Science Foundation of China grant No. 11671214, National Natural Science Foundation of China grant No. 11971348, National Natural Science Foundation of China grant No. 12071230, Natural Science Foundation of Tianjin City grant No. 18JCYBJC16200 and the Natural Science Research Project of Higher Education of Tianjin grant No. 2018KJ148.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Hilbert-Schmidt Operators. (Grochenig [9])A bounded operator is called a Hilbert-Schmidt operator if for some orthonormal basis of . The Hilbert-Schmidt norm of A is given by
and this quantity is independent of the choice of the orthonormal basis . If A is an integral operator with kernel k, that is, , then A is Hilbert-Schmidt if and only if , and in this case .
References
- Folland, G.B. Harmonic Analysis in Phase Space. Annals of Mathematics Studies; Princeton University Press: Princeton, NJ, USA, 1989; Volume 120. [Google Scholar]
- Zhang, Z.C. Unified Wigner-Ville distribution and ambiguity function in the linear canonical transform domain. Signal Process. 2015, 114, 45–60. [Google Scholar] [CrossRef]
- Zhang, Z.C. New Wigner distribution and ambiguity function based on the generalized translation in the linear canonical transform domain. Signal Process. 2016, 118, 51–61. [Google Scholar] [CrossRef]
- Zhang, Z.C.; Yu, T.; Luo, M.K.; Deng, K. Estimating instantaneous frequency based on phase derivative and linear canonical transform with optimized computational speed. IET Signal Process. 2018, 12, 574–580. [Google Scholar] [CrossRef]
- Zhang, Z.C.; Shi, J.; Liu, X.P.; He, L.; Han, M.; Li, Q.Z.; Zhang, N.T. Sampling and reconstruction in arbitrary measurement and approximation spaces associated with linear canonical transform. IEEE Trans. Signal Process. 2016, 64, 6379–6391. [Google Scholar]
- de Gosson, M. Symplectic Geometry and Quantum Mechanics; Birkhuser: Basel, Switzerland, 2006. [Google Scholar]
- Donoho, D.L.; Stark, P.B. Uncertainty principles and signal recovery. Siam J. Appl. Math. 1989, 49, 906–931. [Google Scholar] [CrossRef]
- Ricaud, B.; Torrésani, B. A survey of uncertainty principles and some signal processing applications. Adv. Comput. Math. 2014, 40, 629–650. [Google Scholar] [CrossRef]
- Grochenig, K. Foundations of Time-Frequency Analysis. In Applied and Numerical Harmonic Analysis; Birkhäuser: Basel, Switzerland, 2001. [Google Scholar]
- Zhang, Q. Zak transform and uncertainty principles associated with the linear canonical transform. IET Signal Process. 2016, 10, 791–797. [Google Scholar] [CrossRef]
- Huo, H.Y. Uncertainty Principles for the Offset Linear Canonical Transform. Circuits Syst. Signal Process. 2019, 38, 395–406. [Google Scholar] [CrossRef]
- Zhang, Z.C. Uncertainty principle for real functions in free metaplectic transformation domains. J. Fourier Anal. Appl. 2019, 25, 2899–2922. [Google Scholar] [CrossRef]
- Zhang, Z.C. N-dimensional Heisenberg’s uncertainty principle for fractional Fourier transform. Physics, Engineering, Mathematics. arXiv Preprint 2019, arXiv:1906.05451. Available online: https://arxiv.org/abs/1906.05451 (accessed on 13 June 2019).
- Gordon, E.I.; Kusraev, A.G.; Kutateladze, S.S. Infinitesimal Analysis; Springer Science & Business Media: Cham, Switzerland, 2013. [Google Scholar]
- Bohnenblust, H.F.; Sobczyk, A. Extentions of functional on complex linear spaces. Bull. Am. Math. Soc. 1938, 44, 91–93. [Google Scholar] [CrossRef]
- Li, Y.G.; Li, B.Z.; Sun, H.F. Uncertainty principles for wigner-ville distributionas-sociated with the linear canonical transforms. Abstr. Appl. Anal. 2014, 2014, 1–9. [Google Scholar]
- Nazarov, F.L. Local estimates for exponential polynomials and their applications to inequalities of the uncertainty principle type. Algebra I Anal. 1993, 5, 663–717. [Google Scholar]
- Jaming, P. Nazarov’s uncertainty principle in highter dimension. J. Approx. Theory 2007, 149, 30–41. [Google Scholar] [CrossRef]
- Hardy, G.H. A theorem concerning Fourier transforms. J. Lond. Math. Soc. 1933, 8, 227–231. [Google Scholar] [CrossRef]
- Escauriaza, L.; Kenig, C.E.; Ponce, G.; Vega, L. The sharp Hardy uncertainty principle for Schodinger evolutions. Duke Math. J. 2010, 155, 163–187. [Google Scholar] [CrossRef]
- Bonami, A.; Demange, B.; Jaming, P. Hermite functions and uncertainty principles for the Fourier and the windowed Fourier transforms. Rev. Mat. Iberoam. 2003, 19, 23–55. [Google Scholar] [CrossRef]
- Bagchi, S.C.; Ray, S.K. Uncertainty principles like Hardy’s theorem on some Lie groups. J. Aust. Math. Soc. 1999, A65, 289–302. [Google Scholar] [CrossRef]
- Beckner, W. Pitt’s inequality and the uncertainty principle. Proc. Am. Math. Soc. 1995, 123, 1897–1905. [Google Scholar]
- Folland, G.B.; Sitaram, A. The uncertainty principle: A mathematical survey. J. Fourier Anal. Appl. 1997, 3, 207–238. [Google Scholar] [CrossRef]
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