Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analysis of Generalized Pattern Searches
SIAM Journal on Optimization
DFT-commuting matrix with arbitrary or infinite order second derivative approximation
IEEE Transactions on Signal Processing
Discrete Fractional Fourier Transform Based on New Nearly Tridiagonal Commuting Matrices
IEEE Transactions on Signal Processing
Optimal filtering in fractional Fourier domains
IEEE Transactions on Signal Processing
The discrete rotational Fourier transform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
The discrete fractional Fourier transform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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Determining orthonormal eigenvectors of the DFT matrix, which is closer to the samples of Hermite-Gaussian functions, is crucial in the definition of the discrete fractional Fourier transform. In this work, we disclose eigenvectors of the DFT matrix inspired by the ideas behind bilinear transform. The bilinear transform maps the analog space to the discrete sample space. As jω in the analog s-domain is mapped to the unit circle one-to-one without aliasing in the discrete z-domain, it is appropriate to use it in the discretization of the eigenfunctions of the Fourier transform. We obtain Hermite-Gaussian-like eigenvectors of the DFT matrix. For this purpose we propose three different methods and analyze their stability conditions. These methods include better conditioned commuting matrices and higher order methods.We confirm the results with extensive simulations.