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Linus M. Blässer

dblp:27/2441 · also Linus M. Blaesser · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1986
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 50% Coding theory · 38% Computational complexity · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › boolean functions
walsh spectrum		 0.011986
Walsh power spectrum for wide-sense stationary stochastic processes · IEEE Trans. Inf. Theory 1986
Information theory › probability theory › stochastic processes › stationary processes
wide sense stationary processes		 0.011986
Walsh power spectrum for wide-sense stationary stochastic processes · IEEE Trans. Inf. Theory 1986
Computational complexity › boolean function analysis
fourier spectrum		 0.011986
Walsh power spectrum for wide-sense stationary stochastic processes · IEEE Trans. Inf. Theory 1986
Information theory
linear transformation		 0.011986
Walsh power spectrum for wide-sense stationary stochastic processes · IEEE Trans. Inf. Theory 1986

Methods — techniques the papers use, named apart from their topics

walsh functions · 0.0spectral estimation · 0.0
YearPublicationVenuePosition
1986 Walsh power spectrum for wide-sense stationary stochastic processes
abstract
Existing concepts of Walsh power spectra for wide-sense stationary stochastic processes are restricted to the case of autopower spectra because they are based on real Walsh functions. In this paper a Walsh power spectrum is developed which is based on a system of complex Walsh functions and thus applies to auto and cross power spectra as well. It is shown that this Walsh power spectrum is related to the Fourier power spectrum by a linear transformation. This fact makes it possible to calculate Fourier power spectrum estimates from corresponding Walsh power spectrum estimates. An estimation algorithm for the Walsh power spectrum is given in the paper.
Linus M. Blässer
 IEEE Trans. Inf. Theory1