Research Article | Open Access

Volume 2017 |Article ID 1354129 | https://doi.org/10.1155/2017/1354129

Zhi-Hai Zhuo, "Poisson Summation Formulae Associated with the Special Affine Fourier Transform and Offset Hilbert Transform", Mathematical Problems in Engineering, vol. 2017, Article ID 1354129, 5 pages, 2017. https://doi.org/10.1155/2017/1354129

# Poisson Summation Formulae Associated with the Special Affine Fourier Transform and Offset Hilbert Transform

Accepted16 Jul 2017
Published15 Aug 2017

#### Abstract

This paper investigates the generalized pattern of Poisson summation formulae from the special affine Fourier transform (SAFT) and offset Hilbert transform (OHT) points of view. Several novel summation formulae are derived accordingly. Firstly, the relationship between SAFT (or OHT) and Fourier transform (FT) is obtained. Then, the generalized Poisson sum formulae are obtained based on above relationships. The novel results can be regarded as the generalizations of the classical results in several transform domains such as FT, fractional Fourier transform, and the linear canonical transform.

#### 1. Introduction

The classical Poisson summation formula, which demonstrates that the sum of infinite samples in the time domain of a signal is equivalent to the sum of infinite samples of in the Fourier domain, is of importance in theories and applications of signal processing . The traditional Poisson sum formula can be represented as follows : or where denotes the Fourier transform (FT) of a signal and stands for the sampling interval. Not only does Poisson summation formula play a key role in various branches of the mathematics, but also it finds numerous applications in lots of fields, for example, mechanics, signal processing community, and many other scientific fields. The Poisson summation formula is related to the Fourier transform, and, with the development of modern signal processing technologies, there are many other kinds of transforms that have been proposed, it is therefore worthwhile and interesting to investigate the Poisson sum formula in deep associated with these kinds of new integral transforms.

The special affine Fourier transform (SAFT) [3, 4], also known as the offset linear canonical transform [5, 6] or the inhomogeneous canonical transform , is a six-parameter class of linear integral transform. Many well-known transforms in signal processing and optic systems are its special cases such as Fourier transform (FT), fractional Fourier transform (FRFT), the linear canonical transform (LCT), time shifting and scaling, frequency modulation, pulse chirping, and others [7, 8]. SAFT can be interpreted as a time shifting and frequency modulated version of LCT , that is much more flexible because of its extra parameters . Recently, it has been widely noticed in many practical applications along with the rapid development of LCT . Thus, developing relevant theorems for SAFT is of importance and necessary in optical systems and many signal processing applications as well.

In addition, the generalized Hilbert transform closely related to SAFT, called offset Hilbert transform (OHT), is another powerful tool in the fields of optics and signal processing community . It has been presented recently and widely used for image processing, especially for edge detection and enhancement, because it can emphasize the derivatives of the image [16, 17]. In recent decades, many essential theories and useful applications of SAFT and OHT have been derived from in-depth researching on it [8, 15, 18, 19]. To the best of our knowledge, Poisson sum formula has been generalized with many transforms such as FRFT, LCT, fractional Laplace transform and fractional Hilbert transform [1, 2, 20, 21]. However, none of the research papers throw light on the study of the traditional Poisson sum formula associated with the SAFT and OHT yet. Based on the existing results, the motivation of this paper is to generalize the above-mentioned Poisson sum formula into SAFT and OHT domains.

The rest of this paper is organized as follows. Section 2 gives some fundamental knowledge of SAFT and OHT. In Section 3, we give the relationships between SAFT/OHT and FT in detail. Some novel Poisson summation formulae associated with SAFT are presented in Section 4. Section 5 concludes the paper.

#### 2. Preliminaries

##### 2.1. The Special Affine Fourier Transform

The special affine Fourier transform (SAFT) with real parameters of a signal is defined by the following [5, 22]: where and . Note that, for , the SAFT of a signal is essentially a chirp multiplication and it is of no particular interest for our objective in this work. Hence, without loss of generality, we set in the following section unless stated otherwise. The inverse of an SAFT with parameter is given by an SAFT with parameter , which is where . This can be verified by the definition of SAFT. Most of important transforms can be its special cases when parameter is replaced with specific parameters. For example, when , SAFT coincides with FT; when , SAFT is FRFT; when , SAFT equals LCT. Furthermore, many important theories on SAFT have been investigated [8, 15, 23, 24].

##### 2.2. Offset Hilbert Transform

The offset Hilbert transform (OHT) of a signal is defined as follows : It should be noted that the above definition uses the Cauchy principal value of the integral (denoted here by p.v.). To obtain the relationship between the stand and HT and OHT, we can rewrite (6) as Notice that computing the OHT of a signal is equivalent to multiplying it by a chirp, , then passing the product through a standard Hilbert filter and finally multiplying the output by the chirp, . This relationship between OHT and the classical HT can be shown in Figure 1.

#### 3. The Relationships between SAFT/OHT and Fourier Transform

In order to derive novel Poisson summation formulae based on SAFT and OHT, some relationships between SAFT/OHT and FT are obtained in this section firstly.

Lemma 1. Suppose the SAFT of a signal with parameters is , and set , and then the following relations hold: where is the FT of signal .

Proof. It is easy to verify Lemma 1 by the definitions of SAFT and FT.

Lemma 2. Suppose the SAFT of a signal with parameters is , and set , and then the following relations hold: where is the FT of signal .

Proof. According to the definition of OHT, By replacing , (10) can be rewritten as This completes the proof of Lemma 2.

#### 4. Main Results

Based on the relationships in Lemmas 1 and 2, the generalized Poisson summation formulae associated with SAFT and OHT are derived in following subsections, respectively.

##### 4.1. The Poisson Sum Formula Based on SAFT

Theorem 3. The Poisson summation formulae of a signal in the SAFT domain with parameter are

Proof. If we set , from the traditional Poisson sum formula for in the Fourier domain, that is, (1), we obtain By directly using Lemma 1, we derive that Theorem 3 is proved by simple calculation on (15).

Equations (13) and (15) can be regarded as the generalization of classical Poisson sum formula based on SAFT. It should be noticed that when the parameters of the SAFT are chosen to be the special cases of the SAFT, the derived results reduce to the classical results of Fourier transform domain, fractional Fourier transform domain, and linear canonical transform domains. It clearly demonstrates that the infinite sum of periodic phase-shifted replica of a signal in the time domain is equivalent to the infinite sum of periodic phase-shifted replica in the SAFT domain.

In addition, it is of importance to investigate the Poisson sum formula of signals with compact support in SAFT domain. A signal is said to have compact support in SAFT domain if its SAFT , where is some real number. Without loss of generality, let in the following analysis.

Corollary 4. Suppose a signal is band-limited in SAFT domain with a compact support ; then the Poisson sum formula derived in Theorem 3 can be rewritten as the following forms according to the replica period : (a)When and , (b)When and ,(c)When and ,

Proof. (a) Since is a band-limited signal in SAFT domain with a compact support , it is easy to derive the right hand of (12) that is equal to zeros when . That is, from , we derive that and . Thus, it is easy to derive that Substituting (19) into (12) yields the final results.
(b) It is easy to prove that only when , is nonzero. The right hand of (12) is Substituting (20) into (12) yields the final results.
(c) The proof of this situation is similar to the proof of (a) and (b), and we omit it here.

##### 4.2. The Poisson Sum Formula Based on OHT

Theorem 5. The Poisson sum formula of a signal in the OHT domain with parameter is as follows:

Proof. If we set , it is easy to verify Theorem 5 via (1) and Lemma 2: That is By simple calculation, Theorem 5 is completed.

Equation (21) can be seen as the Poisson sum formula associated with offset Hilbert transform. Furthermore, it is worthwhile and interesting to study the signals with compact support in offset Hilbert transform domain. Let be the OHT of a signal . Then is said to have compact support in OHT domain, if for , where is some real number.

Corollary 6. Suppose signal is band-limited in OHT domain with a compact support ; then the Poisson sum formula derived in Theorem 5 can be rewritten as the following forms according to the replica period : (a)When and , (b)When and , (c)When and ,

Proof. It is easy to verify this corollary using Theorem 5 and the similar method in Corollary 4.

#### 5. Conclusion

In this paper, the traditional Poisson summation formula has been generalized into SAFT and OHT domain. Theorems 3 and 5 are the generalizations of Poisson summation formulae based on SAFT and OHT, respectively. In addition, signals with compact support are mostly used in signal processing and considered in this paper as well. Some novel results associated with Poisson summation formula have been derived in the form of Corollaries 4 and 6.

#### Conflicts of Interest

The author declares that there are no conflicts of interest regarding the publication of this paper.

#### Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (no. 61402044) and Beijing City Board of Education Science and Technology Plan (no. KM201711232009).

1. B.-Z. Li, R. Tao, T.-Z. Xu, and Y. Wang, “The poisson sum formulae associated with the fractional fourier transform,” Signal Processing, vol. 89, no. 5, pp. 851–856, 2009. View at: Publisher Site | Google Scholar
2. J.-F. Zhang and S.-P. Hou, “The generalization of the poisson sum formula associated with the linear canonical transform,” Journal of Applied Mathematics, vol. 2012, Article ID 102039, pp. 1–9, 2012. View at: Publisher Site | Google Scholar
3. S. Abe and J. T. Sheridan, “Generalization of the fractional Fourier transformation to an arbitrary linear lossless transformation: an operator approach,” Journal of Physics. A. Mathematical and General, vol. 27, no. 12, pp. 4179–4187, 1994. View at: Publisher Site | Google Scholar | MathSciNet
4. S. Abe and J. T. Sheridan, “Optical operations on wave functions as the abelian subgroups of the special affine fourier transformation,” Optics Letters, vol. 19, no. 22, pp. 1801–1803, 1994. View at: Publisher Site | Google Scholar
5. S.-C. Pei and J.-J. Ding, “Eigenfunctions of the offset fourier, fractional fourier, and linear canonical transforms,” Journal of the Optical Society of America A. Optics, Image Science, and Vision, vol. 20, no. 3, pp. 522–532, 2003. View at: Publisher Site | Google Scholar | MathSciNet
6. S.-C. Pei and J.-J. Ding, “Eigenfunctions of fourier and fractional fourier transforms with complex offsets and parameters,” IEEE Transactions on Circuits and Systems. I. Regular Papers, vol. 54, no. 7, pp. 1599–1611, 2007. View at: Publisher Site | Google Scholar | MathSciNet
7. L. Z. Cai, “Special affine Fourier transformation in frequency-domain,” Optics Communications, vol. 185, no. 4-6, pp. 271–276, 2000. View at: Publisher Site | Google Scholar
8. Q. Xiang and K. Qin, “Convolution, correlation, and sampling theorems for the offset linear canonical transform,” Signal, Image and Video Processing, vol. 8, no. 3, pp. 433–442, 2014. View at: Publisher Site | Google Scholar
9. J. J. Healy and H. M. Ozaktas, “Sampling and discrete linear canonical transforms,” in Linear canonical transforms, vol. 198, pp. 241–256, Springer, New York, NY, USA, 2016. View at: Publisher Site | Google Scholar | MathSciNet
10. T. Z. Xu and B. Z. Li, Linear Canonical Transform and Its Applications, Science Press, Beijing, BJ, China, 2013.
11. M. Moshinsky and C. Quesne, “Linear canonical transformations and their unitary representations,” Journal of Mathematical Physics, vol. 12, pp. 1772–1780, 1971. View at: Publisher Site | Google Scholar | MathSciNet
12. R. Tao, D. Urynbassarova, and Z. Li B, “The wigner-ville distribution in the linear canonical transform domain,” IAENG International Journal of Applied Mathematics, vol. 46, no. 4, pp. 559–563, 2016. View at: Google Scholar
13. B.-Z. Li, R. Tao, and Y. Wang, “New sampling formulae related to linear canonical transform,” Signal Processing, vol. 87, no. 5, pp. 983–990, 2007. View at: Publisher Site | Google Scholar
14. Q. Feng and B. Li, “Convolution theorem for fractional cosine-sine transform and its application,” Mathematical Methods in the Applied Sciences, vol. 40, no. 10, pp. 3651–3665, 2017. View at: Publisher Site | Google Scholar
15. Q. Xiang, K.-Y. Qin, and Q.-Z. Huang, “Multichannel sampling of signals band-limited in offset linear canonical transform domains,” Circuits, Systems, and Signal Processing, vol. 32, no. 5, pp. 2385–2406, 2013. View at: Publisher Site | Google Scholar | MathSciNet
16. A. W. Lohmann, D. Mendlovic, and Z. Zalevsky, “Fractional Hilbert transform,” Optics Letters, vol. 21, no. 4, pp. 281–283, 1996. View at: Publisher Site | Google Scholar
17. J. A. Davis, D. E. McNamara, and D. M. Cottrell, “Analysis of the fractional Hilbert transform,” Applied Optics, vol. 37, no. 29, pp. 6911–6913, 1998. View at: Publisher Site | Google Scholar
18. N. Goel and K. Singh, “Convolution and correlation theorems for the offset fractional Fourier transform and its application,” AEU - International Journal of Electronics and Communications, vol. 70, no. 2, pp. 138–150, 2016. View at: Publisher Site | Google Scholar
19. A. Bhandari and A. I. Zayed, “Shift-invariant and sampling spaces associated with the fractional Fourier transform domain,” IEEE Transactions on Signal Processing, vol. 60, no. 4, pp. 1627–1637, 2012. View at: Publisher Site | Google Scholar | MathSciNet
20. A. Sheikh and A. Gudadhe, “Poisson summation formulae associated with the generalized fractional Hilbert transform,” Asian Journal of Mathematics and Computer Research, 2016. View at: Google Scholar
21. S. A. Gudadhe and R. P. Deshmukh, “Poisson summation formula associated with the fractional Laplace transform,” Journal of Science and Arts, 2012. View at: Google Scholar
22. A. Stern, “Sampling of compact signals in offset linear canonical transform domains,” Signal, Image and Video Processing, vol. 1, no. 4, pp. 359–367, 2007. View at: Publisher Site | Google Scholar
23. X. Zhi, D. Wei, and W. Zhang, “A generalized convolution theorem for the special affine Fourier transform and its application to filtering,” Optik-International Journal for Light and Electron Optics, vol. 127, no. 5, pp. 2613–2616, 2016. View at: Publisher Site | Google Scholar
24. S. Xu, Y. Chai, Y. Hu, C. Jiang, and Y. Li, “Reconstruction of digital spectrum from periodic nonuniformly sampled signals in offset linear canonical transform domain,” Optics Communications, vol. 348, pp. 59–65, 2015. View at: Publisher Site | Google Scholar

#### More related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.