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The Fractional Fourier Transform: with Applications in Optics and Signal Processing
TLDR
The fractional Fourier transform (FFT) as discussed by the authors has been used in a variety of applications, such as matching filtering, detection, and pattern recognition, as well as signal recovery.Abstract:
Preface. Acknowledgments. Introduction. Signals, Systems, and Transformations. Wigner Distributions and Linear Canonical Transforms. The Fractional Fourier Transform. Time-Order and Space-Order Representations. The Discrete Fractional Fourier Transform. Optical Signals and Systems. Phase-Space Optics. The Fractional Fourier Transform in Optics. Applications of the Fractional Fourier Transform to Filtering, Estimation, and Signal Recovery. Applications of the Fractional Fourier Transform to Matched Filtering, Detection, and Pattern Recognition. Bibliography on the Fractional Fourier Transform. Other Cited Works. Credits. Index.read more
Citations
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Journal ArticleDOI
Error Analysis and Bounds in Time Delay Estimation
TL;DR: Based on the error propagation model, an upper bound to the square of the estimation error is found and the use of the model is demonstrated by two simple examples.
Journal ArticleDOI
Robust Technique for Image Encryption and Decryption Using Discrete Fractional Fourier Transform with Random Phase Masking
Ashutosh,Deepak Sharma +1 more
TL;DR: This paper proposes a novel method of image encryption using discrete fractional Fourier transform (DFRFT) using exponential random phase mask, which makes it almost impossible to retrieve the image without using both the right keys.
Journal ArticleDOI
Optical spectrum encryption in associated fractional Fourier transform and gyrator transform domain
TL;DR: An optical spectrum encryption algorithm for hyperspectral image is proposed in this paper, in which the spatial and spectrum information can be encrypted simultaneously.
Proceedings ArticleDOI
Acoustic Seabed Classification using Fractional Fourier Transform and Time-Frequency Transform Techniques
TL;DR: An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification based on the Fractional Fourier Transform (FrFT), a time-frequency analysis tool which has become attractive in signal processing.
Journal ArticleDOI
Parabolic dictionary learning for seismic wavefield reconstruction across the streamers
TL;DR: Parabolic dictionary learning (parabolic DL) as mentioned in this paper is a dictionary learning method for sparse representation of data, where each learned atom is constrained to represent an elementary waveform that has a constant amplitude along a parabolic travel time moveout.