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A note on the number of squares in a partial word with one hole

Francine Blanchet-Sadri, Robert Mercaş (2009)

RAIRO - Theoretical Informatics and Applications

A well known result of Fraenkel and Simpson states that the number of distinct squares in a word of length n is bounded by 2n since at each position there are at most two distinct squares whose last occurrence starts. In this paper, we investigate squares in partial words with one hole, or sequences over a finite alphabet that have a “do not know” symbol or “hole”. A square in a partial word over a given alphabet has the form uv where u is compatible with v, and consequently, such square is...

A note on tree realizations of matrices

Alain Hertz, Sacha Varone (2007)

RAIRO - Operations Research

It is well known that each tree metric M has a unique realization as a tree, and that this realization minimizes the total length of the edges among all other realizations of M. We extend this result to the class of symmetric matrices M with zero diagonal, positive entries, and such that mij + mkl ≤ max{mik + mjl, mil + mjk} for all distinct i,j,k,l.

A note on univoque self-sturmian numbers

Jean-Paul Allouche (2008)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

We compare two sets of (infinite) binary sequences whose suffixes satisfy extremal conditions: one occurs when studying iterations of unimodal continuous maps from the unit interval into itself, but it also characterizes univoque real numbers; the other is a disguised version of the set of characteristic sturmian sequences. As a corollary to our study we obtain that a real number β in ( 1 , 2 ) is univoque and self-sturmian if and only if the β -expansion of 1 is of the form 1 v , where v is a characteristic...

A note on univoque self-Sturmian numbers

Jean-Paul Allouche (2010)

RAIRO - Theoretical Informatics and Applications

We compare two sets of (infinite) binary sequences whose suffixes satisfy extremal conditions: one occurs when studying iterations of unimodal continuous maps from the unit interval into itself, but it also characterizes univoque real numbers; the other is a disguised version of the set of characteristic Sturmian sequences. As a corollary to our study we obtain that a real number β in (1,2) is univoque and self-Sturmian if and only if the β-expansion of 1 is of the form 1v, where v is a characteristic...

A novel continuous model to approximate time Petri nets: modelling and analysis

Tianlong Gu, Rongsheng Dong (2005)

International Journal of Applied Mathematics and Computer Science

In order to approximate discrete-event systems in which there exist considerable states and events, David and Alla define a continuous Petri net (CPN). So far, CPNs have been a useful tool not only for approximating discrete-event systems but also for modelling continuous processes. Due to different ways of calculating instantaneous firing speeds of transitions, various continuous Petri net models, such as the CCPN (constant speed CPN), VCPN (variable speed CPN) and the ACPN (asymptotic CPN), have...

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Sotirios K. Goudos (2016)

Open Mathematics

A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization...

A novel robust principal component analysis method for image and video processing

Guoqiang Huan, Ying Li, Zhanjie Song (2016)

Applications of Mathematics

The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the ℓ 1 -norm. However, the sparse noise has clustering effect in practice so using a certain ℓ p -norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery...

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