Simplified Computation Method of Useful Functions in the Offset Fractional Fourier Transform
DOI:
https://doi.org/10.24252/msa.v14i1.66372Keywords:
fourier transform, gaussian function, offset fractional fourier transformAbstract
The offset fractional Fourier transform (OFRFT) may be interpreted as a generalization of the classical Fourier transform since the Fourier transform itself represents a particular case of the linear canonical transform. Due to this relationship, the OFRFT has become a useful tool in signal analysis. Several essential properties of the Fourier transform can be extended to the OFRFT domain with appropriate modifications. In this paper, we investigate the analytical connection between the offset fractional Fourier transform and the classical Fourier transform. Based on this relationship, an efficient method for evaluating the Gaussian function in the OFRFT domain is developed. To demonstrate the applicability of the proposed approach, several examples involving Gaussian functions in the OFRFT domain are presented.References
M. Bahri, R. Ashino, An Inequality for Linear Canonical Transform, In: S. L. Peng, M. Favorskaya, H. C. Chao (Eds.), Sensor Networks and Signal Processing, Smart Innovation, Systems and Technologies, vol 176. Springer, Singapore, 2021.
D. Bing, T. Ran, W. Yue, Convolution theorems for the linear canonical transform and their application, Sci. China Inf. Sci. 49(5) (2006) 592–603.
P. Dang, G. T. Deng, T. Qian, A tighter uncertainty principle for linear canonical transform in terms of phase derivative, IEEE Trans. Signal Process. 61(21) (2013) 5153–5164.
L. Debnath, F. A. Shah, Wavelet transforms and their applications, Springer Nature, Switzerland, 2020.
Q. Feng, B. Z. Li, Convolution and correlation theorems for the two-dimensional linear canonical transform and its applications, IET Sig. Process. 10(2) (2016) 125– 132.
N. Goel, K. Singh, R. Saxena, Multiplicative filtering in the linear canonical trans- form domain, IET Sig. Process. 10(2) (2016) 173–181.
Y. Guo, B. Z. Li, Blind image watermarking method based on linear canonical trans- form and QR decomposition, IET Sig. Process. 10(10) (2016) 773–786.
J. J. Healy, P. O’Grady, J. T. Sheridan, Simulating paraxial optical systems using the linear canonical transform: properties, issues and applications, Proc. SPIE 7072, Optics and Photonics for Information Processing II, 70720E, 2008.
Z. J. Huang, S. Cheng, L. H. Gong, N. R. Zhou, Nonlinear optical multi-image en- cryption scheme with two-dimensional linear canonical transform, Opt. Lasers Engg. 124 (2020) 105821.
D. Wei, Filter bank reconstruction of band-limited signals from multichannel samples associated with the LCT, IET Sig. Process 11(3) (2016) 320–331.
D. Wei, H. Hu, Sparse discrete linear canonical transform and its application, Signal Process. 183 (2021) 108046.
D. Wei, Q. Ran, Y. Li, A convolution and convolution theorem for the linear canonical transform and its application, Circuits Syst. Sig. Process. 31(1) (2012) 301–312.
D. Wei, Q. Ran, Y. Li, New convolution theorem for the linear canonical transform and its translation invariance property, Optik-Int. J. Light Electron. Opt. 123(16) (2012) 1478–1481.
Z. C. Zhang, Jittered sampling in linear canonical domain, IEEE Commun. Lett. 24(7) (2020) 1529–1523.
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