Foundations of Signal Processing

By Martin Vetterli, Jelena Kovacevic, Vivek K Goyal. 2014

357 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/07f459b2bc332e445b1419de18cf213205d95e82

How researchers see this paper

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In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Relative Positional Encoding for Transformers with Linear Complexity (2021, neutral): “The variety of attention patterns P ( m − n ) that can be obtained directly depends on the kernel sizes, which is a classical result from signal processing (Vetterli et al., 2014).”
The Exponentially Convergent Trapezoidal Rule (2014, methodology): “The circle is also the setting of the z-transform, an indispensable tool in digital signal processing [174].”
We get the algorithms of our ground truths: Designing referential databases in digital image processing (2017, neutral): “For an accessible introduction to signal processing, see Vetterli et al. (2014).”
Discrete Signal Processing on Graphs: Sampling Theory (2015, methodology): “In Theorem 5.2 in [31], the authors show the recovery for vectors via projection, which lays the theoretical foundation for the classical sampling theory.”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “Those are the basis for data analysis techniques such as Data Mining [39, 46–49], Neural Networks [41, 50–52], Machine Learning [53–55], Signal Processing [56–58] and Visualization Methods [59–61].”
Signal Recovery on Graphs: Variation Minimization (2014, background): “Then, . where (a) follows from submultiplicativity of the spectral norm; and (b) from the norm equivalence [42].”
Understanding the Lomb–Scargle Periodogram (2017, background): “Now, this argument has admittedly been a bit hand-wavy, but there do exist mathematically rigorous approaches to proving that the discrete Fourier transform in Equation 25 captures all of the available frequency information for a uniformly-sampled function gn (see, e.g. Vetterli et al. 2014).”