D
Daniel Potts
Researcher at Chemnitz University of Technology
Publications - 169
Citations - 5948
Daniel Potts is an academic researcher from Chemnitz University of Technology. The author has contributed to research in topics: Fast Fourier transform & Fourier transform. The author has an hindex of 37, co-authored 158 publications receiving 5305 citations. Previous affiliations of Daniel Potts include University of California, Irvine & University of Lübeck.
Papers
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Nonuniform fast Fourier transforms with nonequispaced spatial and frequency data and fast sinc transforms.
TL;DR: In this paper, the error of the fast sinc transform with two sinh-type window functions has an exponential decay with respect to the truncation parameters of the used window functions.
Book ChapterDOI
Chebyshev Methods and Fast DCT Algorithms
TL;DR: This chapter is concerned with Chebyshev methods and fast algorithms for the discrete cosine transform (DCT), which are fundamental for the approximation and integration of real-valued functions defined on a compact interval and the efficient evaluation of polynomials.
Journal ArticleDOI
Modelling vegetation succession in post-industrial ecosystems using vegetation classification in aerial photographs, Buffalo, New York
TL;DR: The authors examined the patterns and rates of succession in PIEs using a novel combination of aerial photo interpretations of 3 to 12-year time series of photos within a 45-year chronosequence from 20 PIE sites in and around Buffalo, New York.
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Transformed rank-1 lattices for high-dimensional approximation
Robert Nasdala,Daniel Potts +1 more
TL;DR: In this approach, algorithms for the evaluation and reconstruction of multivariate trigonometric polynomials on the torus are adapted based on single and multiple reconstructing rank-$1$ lattices based on dimension incremental construction methods for sparse frequency sets.
Posted Content
A sparse FFT approach for ODE with random coefficients
TL;DR: In this article, a general strategy to solve ODEs where some coefficient depend on the spatial variable and on additional random variables is presented, based on the application of a recently developed dimension-incremental sparse fast Fourier transform.