Demo Data from EDBT 2026 / reviewers in the wild / expert
Moshe Tarrab
dblp:46/4263
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1988
—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.
| Computer networks
1 paper |
Physical-layer communications · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › signal processing for communications
adaptive filtering |
0.0 | 1 | 1988 | Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data · IEEE Trans. Inf. Theory 1988 |
Physical-layer communications › signal processing for communications › adaptive filtering
LMS algorithm |
0.0 | 1 | 1988 | Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data · IEEE Trans. Inf. Theory 1988 |
Methods — techniques the papers use, named apart from their topics
stochastic approximation · 0.0mean-square analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian dataabstractIt is demonstrated that the normalized least mean square (NLMS) algorithm can be viewed as a modification of the widely used LMS algorithm. The NLMS is shown to have an important advantage over the LMS, which is that its convergence is independent of environmental changes. In addition, the authors present a comprehensive study of the first and second-order behavior in the NLMS algorithm. They show that the NLMS algorithm exhibits significant improvement over the LMS algorithm in convergence rate, while its steady-state performance is considerably worse.> Moshe Tarrab, Arie Feuer |
IEEE Trans. Inf. Theory | 1 |