Discrete Inequalities on LCT


Linear canonical transform (LCT) is widely used in physical optics, mathematics and information processing. This paper investigates the generalized uncertainty principles, which plays an important role in physics, of LCT for concentrated data in limited supports. The discrete generalized uncertainty relation, whose bounds are related to LCT parameters and data lengths, is derived in theory. The uncertainty principle discloses that the data in LCT domains may have much higher concentration than that in traditional domains.

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Xu, G. , Wang, X. and Xu, X. (2015) Discrete Inequalities on LCT. Journal of Signal and Information Processing, 6, 146-152. doi: 10.4236/jsip.2015.62014.

1. Introduction

In physics, the uncertainty principle plays an important role in elementary fields, and data concentration is often considered carefully via the uncertainty principle [1] - [8] . In continuous signals, the supports are assumed to be

, based on which various uncertainty relations [1] [2] [9] - [21] have been presented. However, in practice, both the supports of time and frequency are often limited. In such case, the support fails to hold

true. In limited supports, some papers such as [22] - [25] have discussed the uncertainty principle in conventional time-frequency domains for continuous and discrete cases and some conclusions are achieved. However, none of them has covered the linear canonical transform (LCT) in terms of Heisenberg uncertainty principles that have been widely used in various fields [4] - [6] . Therefore, there has a great need to discuss the uncertainty relations in LCT domains. As the generalization of the traditional FT, FRFT [5] [6] [26] - [28] and so on, LCT has some special properties with more transform parameters (or freedoms) and sometimes yields the better result [29] . Readers can see more details on LCT in [6] and so on.

2. Preliminaries

2.1. Definition of LCT

Before discussing the uncertainty principle, we will introduce some relevant preliminaries. Here, we first briefly

review the definition of LCT. For given continuous signal and, its LCT [6] is defined as


where and is the complex unit, are the transform parameters defined as that in [6] . In addition, and. If , then and are the LCT transform pairs, i.e., . Also, if, we have the following equations:


However, unlike the discrete FT, there are a few definitions for the DLCT (discrete LCT), but not only one. In this paper, we will employ the definition defined as follows [6] :


Clearly, if, (2) reduces to the traditional discrete FT [6] . Also, we can rewrite definition (2) as with and, where,.

For DLCT, we have the following property [5] [6] :


More details on DLCT can be found in [6] .

2.2. Frequency-Limiting Operators

Definition 1: Let be a complex-valued signal with and its LCT, if there is a function vanishing outside (is a measurable set) such that, then is -concentrated.

Specially, if, then definition 1 reduces to the case in time domain [22] [23] . If, then definition 1 reduces to the case in traditional frequency domain [22] [23] .

Definition 2: Generalized frequency-limiting operator is defined as


If, then definition 2 is the time-limiting operator [22] [23] . If, then definition 2 is the traditional frequency-limiting operator [22] [23] . Definitions 1 and 2 disclose the relation between and. For the discrete case, we have the following definitions.

Definition 3: Let be a discrete sequence with and its DLCT, if there is a sequence satisfying such that, then is -concentrated. Here, is the 0-norm operator that counts the non-zero elements.

Definition 4: Generalized discrete frequency-limiting operator is defined as

with is the DLCT of and is the character function on.

Clearly, definitions 3 and 4 are the discrete extensions of definitions 1 and 2. They have the similar physical meaning. These definitions are introduced for the first time, the traditional cases [22] [23] are only their special cases. Definition 3 and 4 disclose the relation between and.

3. The Uncertainty Relations

3.1. The Uncertainty Principle

First let us introduce a lemma.

Lemma 3:

where is the Frobenius matrix norm.

Proof: From the definition of the operator in definition 4, we have


Exchange the locations of the sum operators, we obtain

Hence, according to the definition of the Frobenius matrix norm [1] and the definition of DLCT, we have

In the similar manner with the continuous case, we can obtain. Since, we have , thus, we get . Therefore, we can obtain the following theorem 2.

Theorem 2: Let be the DLCT of the time sequence for transform parameter, with -concentrated on index set . Let be the numbers of nonzero entries in (respectively). Then


3.2. Extensions

Set in theorem 2, we can obtain the following theorem 3 directly.

Theorem 3: Let be the DLCT of the time sequence with

length N. counts the numbers of nonzero entries in (respectively). Then

Clearly, theorem 3 is a special case of theorem 2. Also, this theorem can be derived via theorem 1 in [25] .

Differently, we obtain this result in a different way. Here we note that since, there is at least one non-zero element in every LCT domain for. Therefore, for.

Through setting special value for in theorem 3, we have

Corollary 1: with.

We can obtain the following more general uncertainty relation associated with DLCT.

Theorem 4: Let be the DLCT of the time sequence (and) with length and. counts the number of nonzero elements in. Then


Proof: From the assumption and the definition of DLCT [6] , we know


where . Therefore, let, have [25]

where and with and with.

Hence, we obtain

Set, then

Using the triangle inequality, we have


From and Parseval’s principle [6] , we obtain:




Therefore, we obtain

Adding all the above inequalities, we have with. Similarly, from and Parseval’s principle [6] , we obtain, hence


From the definition and property of DLCT [6] we have


Hence, we finally obtain with. This theorem is the extension of theorem 3 and discloses the uncertainty relation between multiple signals.

4. Conclusion

In practice, for the discrete data, not only the supports are limited, but also they are sequences of data points whose number of non-zero elements is countable accurately. This paper discussed the generalized uncertainty relations on LCT in terms of data concentration. We show that the uncertainty bounds are related to the LCT parameters and the support lengths. These uncertainty relations will enrich the ensemble of uncertainty principles and yield the potential illumination for physics.


This work was fully supported by the NSFCs (61002052 and 61471412) and partly supported by the NSFC (61250006).


This work was fully supported by the NSFC (61002052) and partly supported by the NSFC (61250006 and 60975016).

Conflicts of Interest

The authors declare no conflicts of interest.


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