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Miriam E. Ramirez

dblp:353/1841 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0008-4740-5992ORCID · corroborated

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

Security and privacy · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021

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
Coding theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › sequences › binary sequences
merit factor
0.912025
Limiting Moments of Autocorrelation Demerit Factors of Binary Sequences · IEEE Trans. Inf. Theory 2025
Coding theory › sequences
sequence design
0.912025
Limiting Moments of Autocorrelation Demerit Factors of Binary Sequences · IEEE Trans. Inf. Theory 2025

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

quasi-polynomial analysis · 0.9combinatorial enumeration · 0.9
YearPublicationVenuePosition
2025 Moments of autocorrelation demerit factors of binary sequences
Daniel J. Katz, Miriam E. Ramirez
Des. Codes Cryptogr.2
2025 Limiting Moments of Autocorrelation Demerit Factors of Binary Sequences
abstract
Various problems in engineering and natural science demand binary sequences that do not resemble translates of themselves, that is, the sequences must have small aperiodic autocorrelation at every nonzero shift. If f is a sequence, then the demerit factor of f is the sum of the squared magnitudes of the autocorrelations at all nonzero shifts for the sequence obtained by normalizing f to unit Euclidean norm. The demerit factor is the reciprocal of Golay’s merit factor, and low demerit factor indicates low self-similarity of a sequence under translation. We endow the$2^{\ell } $binary sequences of length$\ell $with uniform probability measure and consider the distribution of their demerit factors. Earlier works used combinatorial techniques to find exact formulas for the mean, variance, skewness, and kurtosis of the distribution as a function of$\ell $. These revealed that for$\ell \geq 4$, the pth central moment of this distribution is strictly positive for every$p \geq 2$. This article shows that for every p, the pth central moment is$\ell ^{-2 p}$times a quasi-polynomial function of$\ell $with rational coefficients. It also shows that, in the limit as$\ell $tends to infinity, the pth standardized moment is the same as that of the standard normal distribution.
Daniel J. Katz, Miriam E. Ramirez
IEEE Trans. Inf. Theory2