1. A general criterion for pointwise Fourier inversion 2. Pointwise Fourier inversion on R n (n = 3) 3. Fourier inversion on R 2 4. Fourier inversion on R n (general n) 5. Fourier inversion on spheres 6. Fourier inversion on complex projective space, and variants 7. Fourier inversion on hyperbolic space, and variants 8. Fourier inversion on strongly scattering manifolds 9. Hermite expansions an...

In the next two lectures, we will continue to expand our toolbox for computing in frequency domain, by adding two important computational tools. The first, the Fast Fourier Transform (FFT) is a family of algorithms for efficiently computing the Discrete Fourier Transform. These algorithms are of great practical importance, as theymake it possible to perform frequency domain analysis of very lar...

Four Fourier transforms are usually defined, the Integral Fourier transform, the Discrete-Time Fourier transform (DTFT), the Discrete Fourier transform (DFT) and the Integral Fourier transform for periodic functions. However, starting from their definitions, we show that all four Fourier transforms can be reduced to actually only one Fourier transform, the Fourier transform in the distributiona...

This paper is part 4 in a series of papers about the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). The focus of this paper is on spectral leakage. Spectral leakage applies to all forms of DFT, including the FFT (Fast Fourier Transform) and the IFFT (Inverse Fast Fourier Transform). We demonstrate an approach to mitigating spectral leakage based on windowing...

--The fractional fourier transform(FRFT) which is a generalisation of classical fourier transform, was introduced number of years ago in the mathematics literature but appears to have remained largely unknown to a signal processing community to which it may, however, be a potentially useful. The fractional fourier transform depends on a parameter ‘α’ and can be interpreted as a rotation by an a...

where w(n) is an analysis window. At each time instant, the STFT is a discrete-time Fourier transform or discrete Fourier transform (DFT). Many DFT computing algorithms and architectures have been proposed. However, the direct use of the DFI' without considering the special sliding window effect of STFT is not an efficient approach. There are two major approaches for STFT. One is based on the f...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

Wavelet transformation is one of the most practical mathematical transformations in the field of image processing, especially image and signal processing. Depending on the nature of the multiresolution analysis, Wavelet transformation become more accessible and powerful tools. In this paper, we refer to the mathematical foundations of this transformation.   Introduction: The...

With efficient tools for computing the Fourier transform in hand, in this lecture we briefly describe issues that arise when we try to use this tool to analyze signals. We will discuss two major issues. The first is the effect of only collecting a finite number of samples x[0], . . . , x[L − 1], rather than observing x[n] for all n. The second is what happens when the signal of interest has fre...